18 Statistical Inference Jobs

Job Posting TitleSR. DATA SCIENTIST Band/Level5-2-C Education ExperienceBachelors Degree (High School +4 years) Employment Experience5-7 years At TE, you will unleash your potential working with people from diverse backgrounds and industries to create a safer, sustainable and more connected world. Job Overview Solves complex problems and help stakeholders make data- driven decisions by leveraging quantitative methods, such as machine learning. It often involves synthesizing large volume of information and extracting signals from data in a programmatic way. Roles & Responsibilities Key Responsibilities Design, train, and evaluate supervised & unsupervised models (regression, classification, clustering, uplift). Apply automated hyperparameter optimization (Optuna, HyperOpt) and interpretability techniques (SHAP, LIME). Perform deep exploratory data analysis (EDA) to uncover patterns & anomalies. Engineer predictive features from structured, semistructured, and unstructured data; manage feature stores (Feast). Ensure data quality through rigorous validation and automated checks. Build hierarchical, intermittent, and multiseasonal forecasts for thousands of SKUs. Implement traditional (ARIMA, ETS, Prophet) and deeplearning (RNN/LSTM, TemporalFusion Transformer) approaches. Reconcile forecasts across product/category hierarchies; quantify accuracy (MAPE, WAPE) and bias. Establish model tracking & registry (MLflow, SageMaker Model Registry). Develop CI/CD pipelines for automated retraining, validation, and deployment (Airflow, Kubeflow, GitHub Actions). Monitor data & concept drift; trigger retuning or rollback as needed. Design and analyze A/B tests, causal inference studies, and Bayesian experiments. Provide statisticallygrounded insights and recommendations to stakeholders. Translate business objectives into datadriven solutions; present findings to exec & nontech audiences. Mentor junior data scientists, review code/notebooks, and champion best practices. Desired Candidate Minimum Qualifications M.S. in Statistics (preferred) or related field such as Applied Mathematics, Computer Science, Data Science. 5+ years building and deploying ML models in production. Expertlevel proficiency in Python (Pandas, NumPy, SciPy, scikitlearn), SQL, and Git. Demonstrated success delivering largescale demandforecasting or timeseries solutions. Handson experience with MLOps tools (MLflow, Kubeflow, SageMaker, Airflow) for model tracking and automated retraining. Solid grounding in statistical inference, hypothesis testing, and experimental design. Preferred / NicetoHave Experience in supplychain, retail, or manufacturing domains with highgranularity SKU data. Familiarity with distributed data frameworks (Spark, Dask) and cloud data warehouses (BigQuery, Snowflake). Knowledge of deeplearning libraries (PyTorch, TensorFlow) and probabilistic programming (PyMC, Stan). Strong datavisualization skills (Plotly, Dash, Tableau) for storytelling and insight communication. Competencies ABOUT TE CONNECTIVITY TE Connectivity plc (NYSETEL) is a global industrial technology leader creating a safer, sustainable, productive, and connected future. Our broad range of connectivity and sensor solutions enable the distribution of power, signal and data to advance next-generation transportation, energy networks, automated factories, data centers, medical technology and more. With more than 85,000 employees, including 9,000 engineers, working alongside customers in approximately 130 countries, TE ensures that EVERY CONNECTION COUNTS. Learn more atwww.te.com and onLinkedIn , Facebook , WeChat, Instagram and X (formerly Twitter). WHAT TE CONNECTIVITY OFFERS: We are pleased to offer you an exciting total package that can also be flexibly adapted to changing life situations - the well-being of our employees is our top priority! Competitive Salary Package Performance-Based Bonus Plans Health and Wellness Incentives Employee Stock Purchase Program Community Outreach Programs / Charity Events IMPORTANT NOTICE REGARDING RECRUITMENT FRAUD TE Connectivity has become aware of fraudulent recruitment activities being conducted by individuals or organizations falsely claiming to represent TE Connectivity. Please be advised that TE Connectivity never requests payment or fees from job applicants at any stage of the recruitment process. All legitimate job openings are posted exclusively on our official careers website at te.com/careers, and all email communications from our recruitment team will come only from actual email addresses ending in @te.com . If you receive any suspicious communications, we strongly advise you not to engage or provide any personal information, and to report the incident to your local authorities. Across our global sites and business units, we put together packages of benefits that are either supported by TE itself or provided by external service providers. In principle, the benefits offered can vary from site to site.

We are searching for a talented Document Extraction and Inference Engineer who possesses expertise in traditional machine learning algorithms and rule-based NLP techniques. The ideal candidate will have a solid background in document processing, structured data extraction, and inference modeling using classical ML approaches. Your primary responsibility will involve designing, implementing, and optimizing document extraction pipelines for various applications with a focus on ensuring accuracy and efficiency. Key Responsibilities - Developing and implementing document parsing and structured data extraction techniques. - Utilizing OCR (Optical Character Recognition) and pattern-based NLP for text extraction. - Building Foundational Models (NOT LLM's) to address inference problems. - Optimizing rule-based and statistical models for document classification and entity recognition. - Designing feature engineering strategies to enhance inference accuracy. - Working with structured and semi-structured data (PDFs, scanned documents, XML, JSON). - Implementing knowledge-based inference models for decision-making applications. - Collaborating with data engineers to construct scalable document processing pipelines. - Conducting error analysis and enhancing extraction accuracy through iterative refinements. - Keeping abreast of advancements in traditional NLP and document processing techniques. Required Qualifications - Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or a related field. - 3+ years of experience in document extraction and inference modeling. - At least 5+ years of overall experience. - Strong proficiency in Python and ML libraries (Scikit-learn, NLTK, OpenCV, Tesseract). - Expertise in OCR technologies, regular expressions, and rule-based NLP. - Experience with SQL and database management for handling extracted data. - Knowledge of probabilistic models, optimization techniques, and statistical inference. - Familiarity with cloud-based document processing (AWS Textract, Azure Form Recognizer). - Strong analytical and problem-solving skills. Preferred Qualifications - Experience with graph-based document analysis and knowledge graphs. - Knowledge of time series analysis for document-based forecasting. - Exposure to reinforcement learning for adaptive document processing. - Understanding of the credit/loan processing domain. Location: Chennai, India.,

Job Title: Senior Data Scientist Intelligent Offer Management Location: Bangalore (3 days work from office) Type: Hybrid Full-time (On-Site) Experience Level: 3–6 years About the Role: We are hiring a Data Scientist to lead and scale our Intelligent Offer Management engine – a key pillar of our revenue optimization strategy. This role sits at the intersection of data science, pricing strategy, product thinking, and experimentation, enabling personalized, dynamic, and contextual offer decisions at scale. You'll work closely with product managers, business teams, and engineers to build models that recommend the right offer to the right customer at the right time. Key Responsibilities: Design and implement real-time offer recommendation models using historical purchase, engagement, and behavioral data. Develop algorithms for dynamic pricing, offer personalisation, segmentation, and discount optimisation. Collaborate with cross-functional stakeholders to translate business requirements into data science solutions. Conduct A/B and multivariate experiments to evaluate offer performance and optimise offer strategies. Build predictive models for conversion propensity, elasticity curves, and uplift measurement. Package models for production deployment, ensuring scalability, performance, and monitoring. Influence product design and customer journeys with data-driven insights. Must-Have Qualifications: 3-6 years of experience in data science, quantitative analytics, or pricing strategy roles. Proficiency in Python and SQL, with experience in building, validating, and deploying ML models. Deep understanding of offer science, pricing elasticity, behavioral economics, and incentive systems. Strong business acumen with the ability to balance accuracy with impact and speed. Experience working with experimentation frameworks and statistical inference. Ability to communicate complex insights effectively to both technical and non-technical stakeholders.

As a Principal Scientist within the Preclinical Safety (PCS) Pathology Team at Novartis, you will play a crucial role in advancing translational safety omics data analysis to support innovative drug discovery and development efforts. Your responsibilities will involve collaborating closely with pathologists, bench scientists, and subject matter experts across different Novartis sites such as the USA and Switzerland. Your expertise in omics-based data science and your dedication to enhancing multimodal data analysis in translational drug safety will be key to success in this role. Your day-to-day tasks will include using machine learning and statistical methods to analyze spatial transcriptomics and spatial proteomics data from raw reads, comparing and contrasting different omics data sets, conducting multimodal analysis with other data modalities, and supporting various projects with data science expertise in fields like gene and cell therapy, target discovery, genetics, drug safety, and compound screening. Additionally, you will be expected to innovate by leveraging Data Science and Artificial Intelligence to solve problems effectively, communicate regularly with stakeholders, evaluate new technologies and scientific software, and independently identify and apply research articles to business problems. To qualify for this position, you should hold a Master's or Ph.D. in Data Science, Computational Biology, Bioinformatics, or a related discipline. You must be proficient in programming languages and data science workflows such as Python, R, Git, UNIX command line, and high-performance computing clusters. A minimum of 5 years of experience in analyzing large biological datasets in a drug discovery/development or academic setting is required, along with proven expertise in exploratory data analysis, statistical inference, and machine learning algorithms. Your collaborative mindset, exceptional communication skills, and ability to work in an agile environment will be essential for success in this role. Novartis is a company dedicated to improving and extending people's lives, with a strong belief in the power of medical science and digital innovation. The organization values diversity, equity, and inclusion, fostering an environment that encourages new ways of working and collaboration. By joining Novartis, you will have the opportunity to work in an unbossed culture that promotes integrity, curiosity, and flexibility, allowing you to tackle challenging medical problems with courage and ambition. Novartis is committed to creating an inclusive work environment that reflects the diversity of the patients and communities it serves. If you are interested in exploring career opportunities at Novartis, you can join the Novartis Network to stay connected and receive updates on future roles that align with your goals. Novartis offers a range of benefits and rewards to support your personal and professional growth. To learn more about the opportunities available and the company's commitment to helping you thrive, you can refer to the Novartis handbook on benefits and rewards.,

Senior Data Scientist Req number: R5797 Employment type: Full time Worksite flexibility: Hybrid Who we are CAI is a global technology services firm with over 8,500 associates worldwide and a yearly revenue of $1 billion+. We have over 40 years of excellence in uniting talent and technology to power the possible for our clients, colleagues, and communities. As a privately held company, we have the freedom and focus to do what is right—whatever it takes. Our tailor-made solutions create lasting results across the public and commercial sectors, and we are trailblazers in bringing neurodiversity to the enterprise. Job Summary We’re searching for an experienced Senior Data Scientist who excels at statistical analysis, feature engineering, and end to end machine learning operations. Your primary mission will be to build and productionize demand forecasting models across thousands of SKUs, while owning the full model lifecycle—from data discovery through automated re training and performance monitoring. This is a Full-time and Hybrid position. Job Description What You’ll Do Advanced ML Algorithms: Design, train, and evaluate supervised & unsupervised models (regression, classification, clustering, uplift). Apply automated hyperparameter optimization (Optuna, HyperOpt) and interpretability techniques (SHAP, LIME). Data Analysis & Feature Engineering: • Perform deep exploratory data analysis (EDA) to uncover patterns & anomalies. Engineer predictive features from structured, semistructured, and unstructured data; manage feature stores (Feast). Ensure data quality through rigorous validation and automated checks. TimeSeries Forecasting (Demand): • Build hierarchical, intermittent, and multiseasonal forecasts for thousands of SKUs. Implement traditional (ARIMA, ETS, Prophet) and deeplearning (RNN/LSTM, TemporalFusion Transformer) approaches. Reconcile forecasts across product/category hierarchies; quantify accuracy (MAPE, WAPE) and bias. MLOps & Model Lifecycle: • Establish model tracking & registry (MLflow, SageMaker Model Registry). Develop CI/CD pipelines for automated retraining, validation, and deployment (Airflow, Kubeflow, GitHub Actions). Monitor data & concept drift; trigger retuning or rollback as needed. Statistical Analysis & Experimentation: • Design and analyze A/B tests, causal inference studies, and Bayesian experiments. Provide statisticallygrounded insights and recommendations to stakeholders. Collaboration & Leadership: Translate business objectives into datadriven solutions; present findings to exec & nontech audiences. Mentor junior data scientists, review code/notebooks, and champion best practices. What You'll Need M.S. in Statistics (preferred) or related field such as Applied Mathematics, Computer Science, Data Science. 5+ years building and deploying ML models in production. Expertlevel proficiency in Python (Pandas, NumPy, SciPy, scikitlearn), SQL, and Git. Demonstrated success delivering largescale demandforecasting or timeseries solutions. Handson experience with MLOps tools (MLflow, Kubeflow, SageMaker, Airflow) for model tracking and automated retraining. Solid grounding in statistical inference, hypothesis testing, and experimental design. Experience in supplychain, retail, or manufacturing domains with highgranularity SKU data. Familiarity with distributed data frameworks (Spark, Dask) and cloud data warehouses (Big Query, Snowflake). Knowledge of deeplearning libraries (PyTorch, TensorFlow) and probabilistic programming (PyMC, Stan). Strong datavisualization skills (Plotly, Dash, Tableau) for storytelling and insight communication. Physical Demands This role involves mostly sedentary work, with occasional movement around the office to attend meetings, etc. Ability to perform repetitive tasks on a computer, using a mouse, keyboard, and monitor. Reasonable accommodation statement If you require a reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employment selection process, please direct your inquiries to application.accommodations@cai.io or (888) 824 – 8111.

Job Description: Dreaming big is in our DNA. It's who we are as a company, our culture, our heritage, and more than ever, our future. A future where we're always looking forward, serving up new ways to meet life's moments, and where we keep dreaming bigger. We look for individuals with passion, talent, and curiosity and provide them with teammates, resources, and opportunities to unleash their full potential. The power we create together, when we combine your strengths with ours, is unstoppable. Are you ready to join a team that dreams as big as you do AB InBev GCC, incorporated in 2014 as a strategic partner for Anheuser-Busch InBev, leverages the power of data and analytics to drive growth for critical business functions such as operations, finance, people, and technology. The teams are transforming Operations through Tech and Analytics. Do you dream big We need you. Job Title: Senior Data Scientist Location: Bangalore Reporting to: Senior Manager Purpose of Role: - Understand and solve complex business problems with sound analytical prowess to provide impactful insights in decision-making. - Ensure any implementation roadblocks are communicated to the Analytics Manager to prevent project timeline delays. - Document every aspect of the project in a standard way for future reference. - Articulate technical complexities to senior leadership in a simple and easy-to-understand manner. Key Tasks and Accountabilities: - Understand business problems and collaborate with stakeholders to translate them into data-driven analytical/statistical problems during the solution-building process. - Create appropriate datasets and develop statistical data models. - Translate complex statistical analyses over large datasets into insights and actionable steps. - Analyze results and present findings to stakeholders. - Communicate insights using business-friendly presentations. - Mentor other Data Scientists/Associate Data Scientists. - Build a project pipeline in Databricks that is production-ready. - Develop dashboards (preferably in Power BI) for easy consumption of solutions. Qualifications, Experience, Skills: - Level of Educational Attainment Required: Bachelor's/Master's Degree in Statistics, Applied Statistics, Economics, Econometrics, Operations Research, or any other quantitative discipline. - Previous Work Experience: Minimum 4-6 years in a data science role, building, implementing, and operationalizing end-to-end solutions. - Expertise strongly desired in building statistical and machine learning models for classification, regression, forecasting, anomaly detection, dimensionality reduction, clustering, etc. - Exposure to optimization and simulation techniques (good to have). - Expertise in building NLP-based language models, sentiment analysis, text summarization, and Named Entity Recognition. - Proven skills in translating statistics into insights, with sound knowledge in statistical inference and hypothesis testing. - Mandatory: Microsoft Office, Expert in Python, Advanced Excel. - An undying love for beer!,

Are you passionate about the intersection of data, technology, and science, and excited by the potential of Real-World Data (RWD) and AI Do you thrive in collaborative environments and aspire to contribute to the discovery of groundbreaking medical insights If so, join the data42 team at Novartis! At Novartis, we reimagine medicine by leveraging state-of-the-art analytics and our extensive internal and external data resources. Our data42 platform grants access to high-quality, multi-modal preclinical and clinical data, along with RWD, creating the optimal environment for developing advanced AI/ML models and generating health insights. Our global team of data scientists and engineers utilizes this platform to uncover novel insights and guide drug development decisions. As an RWD SME / RWE Execution Data Scientist, you will focus on executing innovative methodologies and AI models to mine RWD on the data42 platform. You will be the go-to authority for leveraging diverse RWD modalities patterns crucial to understanding patient populations, biomarkers, and drug targets, accelerating the development of life-changing medicines. Duties and Responsibilities: - Collaborate with R&D stakeholders to co-create and implement innovative, repeatable, scalable, and automated data and technology solutions in line with data42 strategy. - Be a data Subject Matter Expert (SME), understand Real World Data (RWD) of different modalities, vocabularies (LOINC, ICD, HCPCS, etc.), non-traditional RWD (Patient reported outcomes, Wearables and Mobile Health Data) and where and how they can be used, including in conjunction with clinical data, omics data, pre-clinical data, and commercial data. - Contribute to data strategy implementation such as Federated Learning, tokenization, data quality frameworks, regulatory requirements (submission data to HL7 FHIR formats conversion, Sentinel initiative), conversion to common data models and standards (OMOP, FHIR, SEND, etc.), FAIR principles, and integration with enterprise catalog. - Define and execute advanced integrated and scalable analytical approaches and research methodologies (including industry trends) in support of exploratory and regulatory use of AI models for RWD analysis across the Research Development Commercial continuum by facilitating research questions. - Stay current with emerging applications and trends, driving the development of advanced analytic capabilities for data42 across the Real-world evidence generation lifecycle, from ideation to study design and execution. - Demonstrate high agility working across various cross-located and cross-functional associates across business domains (commercial, Development, Biomedical Research) or Therapeutic area divisions for our priority disease areas to execute complex and critical business problems with quantified business impact/ROI. Ideal Candidate Profile: - PhD or MSc. in a quantitative discipline (e.g., but not restricted to Computer Science, Physics, Statistics, Epidemiology) with proven expertise in artificial Intelligence / Machine Learning. - 8+ years of relevant experience in Data Science (or 4+ years post-qualification in case of PhD). - Extensive experience in Statistical and Machine Learning techniques: Regression, Classification, Clustering, Design of Experiments, Monte Carlo Simulations, Statistical Inference, Feature Engineering, Time Series Forecasting, Text Mining, and Natural Language Processing, LLMs, and multi-modal Generative AI. - Good to have skills: Stochastic models, Bayesian Models, Markov Chains, Optimization techniques including, Dynamic Programming Deep Learning techniques on structured and unstructured data, Recommender Systems. - Proficiency in tools and packages: Python, R(optional), SQL; exposure to dashboard or web-app building using PowerBI, R-Shiny, Flask, open source or proprietary software and packages is an advantage. - Knowledge in data standards e.g. OHDSI OMOP, and other data standards, FHIR HL7 for regulatory, and best practices. - Good to have: Foundry, big data programming, working knowledge of executing data science on AWS, DataBricks, or SnowFlake. - Strong in Matrix collaboration environments with good communication and collaboration skills with country/ regional/ global stakeholders in an individual contributor capacity. Novartis is committed to building an outstanding, inclusive work environment and diverse teams representative of the patients and communities we serve. Why Novartis: Helping people with disease and their families takes more than innovative science. It takes a community of smart, passionate people like you. Collaborating, supporting, and inspiring each other. Combining to achieve breakthroughs that change patients" lives. Ready to create a brighter future together Join our Novartis Network: Not the right Novartis role for you Sign up to our talent community to stay connected and learn about suitable career opportunities as soon as they come up. Benefits and Rewards: Read our handbook to learn about all the ways we'll help you thrive personally and professionally.,

Capgemini Invent Capgemini Invent is the digital innovation, consulting and transformation brand of the Capgemini Group, a global business line that combines market leading expertise in strategy, technology, data science and creative design, to help CxOs envision and build whats next for their businesses. Your role Solution Design:Collaborate with stakeholders to design and implement effective Generative AI solutions tailored to specific business requirements, ensuring alignment with industry best practices. Framework Development:Build and enhance frameworks for deploying Generative AI applications, ensuring they are robust, scalable, and optimized for hyper-scaler environments. LLM Integration:Work extensively with Large Language Models, optimizing their performance for various applications and use cases, while leveraging the capabilities of hyper-scaler platforms. Deep learning and ML expertiseExcellent understanding and hand-on experience of data-science and machine learning techniques & algorithms for supervised & unsupervised problems, NLP and computer vision. Good applied statistics skills, such as distributions, statistical inference & testing, etc. Hands-On Development:Engage in hands-on coding and development of AI models and algorithms, utilizing the latest tools and technologies in the Generative AI landscape. Proof of Concept (PoC) Development:Lead the creation of PoCs that demonstrate the capabilities of Generative AI solutions. Your Profile Education:Bachelors degree in Computer Science, Software Engineering, or a related field; Masters degree is a plus. Experience:Hands-on experience in AI/ML development, with a strong emphasis on Generative AI technologies in hyper-scaler environments Technical Skills: Proficiency in programming languages such as Python, TensorFlow, or PyTorch; experience with LLM frameworks and model deployment on platforms like AWS, Azure, or Google Cloud. What you will love about working here We recognize the significance of flexible work arrangements to provide support. Be it remote work, or flexible work hours, you will get an environment to maintain healthy work life balance. At the heart of our mission is your career growth. Our array of career growth programs and diverse professions are crafted to support you in exploring a world of opportunities. Equip yourself with valuable certifications in the latest technologies such as Generative AI. About Capgemini Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, cloud and data, combined with its deep industry expertise and partner ecosystem. The Group reported 2023 global revenues of 22.5 billion.

Job Summary: This entry level/early career professional, applied science position utilizes the core competencies of thermal and fluid sciences, using and advancing analytical and experimental approaches to answer key questions about the performance and architecture of a system or component. This drives decisions and creates technical content which ultimately leads to state of the art, highly effective, environmentally friendly, and competitive products. Key Responsibilities: Investigates product, system, and/or technology problems that are related to thermal and fluid sciences, understands causal mechanisms, recommends appropriate action, owns problem resolution and documents results with guidance from more experienced team members. Applies and supports the improvement of processes such as Pre Program Planning, (PPP), Product Preceding Technology, (PPT), Value Package Introduction (VPI), or Value Package Change, (VPC), Engineering Standard Work, (ESW), Functional Excellence Practice (FEP) and tools related to thermal and fluid sciences such as GT-Suite, Boost, Fluent, Converge, Concerto, CyberApps (virtual application model), Cummins Data Analysis Tool (CDAT), Mach, test data calculation, DARTS, test data storage, Calterm, Electronic Control Module (ECM) calibration tool, Emissions data system, Integrity, and ACCEPTS ECM in-loop test bench required to support the processes and enable high quality decision making. Obtains input and negotiates with immediate stakeholders, such as team members, technical managers, project leaders, other Thermal Sciences engineers, product and manufacturing engineers, and supplier partners and delivers information and recommendations to support project decisions, or to provide input related to further analysis, design optimization, feasibility studies, or architecture definition. Makes decisions in the areas of day to day project details, such as conducting analysis, testing, and communication with stakeholders that impact and support the successful progression of projects. Owns problem resolution for moderately complex components, products, systems, subsystems or services with technical complexity and ambiguity increasing as experience is gained in the role. Provides independent execution of established work processes and systems, while still developing technology or product knowledge; engages with the improvement of systems and processes. Involves minimal direct management of people, but could involve the coordination and direction of work amongst technicians and/or temporary student employees. Contributes effectively toward team goals, exhibits influence within a work group and continues to develop proficiency in the competency areas critical to success in the role. External Qualifications and Competencies Competencies: Applies Principles of Fluid Dynamics and Heat Transfer - Interprets and predicts the performance of fluid and thermal systems by leveraging a quantitative and intuitive comprehension of the flow of liquids and gases, the transfer of thermal energy between elements of a system, and the application of modeling and experimental tools, to answer key questions about the system or component. Applies Principles of Thermodynamics and Energy Conversion Systems - Interprets and predicts the performance of energy conversion systems such as IC engines, gas turbines, fuel cells, and batteries, by leveraging a quantitative and intuitive comprehension of thermodynamics, fluid mechanics, modeling tools, controls, and experimental tools to answer key questions about the system or component. Product Failure Mode Avoidance - Mitigates potential product failure modes, by identifying interfaces, functions, functional requirements, interactions, control factors, noise factors, and prioritized potential failure modes and potential failure causes for the system of interest to effectively and efficiently improve the reliability of Cummins products. Product Function Modeling, Simulation and Analysis - Impacts product design decisions through the utilization and/or interpretation of computational tools and methods that predict the capability of a product's function relative to its system, sub-system and/or component level requirements. Product Problem Solving - Solves product problems using a process that protects the customer; determines the assignable cause; implements robust, data-based solutions; and identifies the systemic root causes and recommended actions to prevent problem reoccurrence. System Requirements Engineering - Uses appropriate methods and tools to translate stakeholder needs into verifiable requirements to which designs are developed; establishes acceptance criteria for the system of interest through analysis, allocation and negotiation; tracks the status of requirements throughout the system lifecycle; assesses the impact of changes to system requirements on project scope, schedule, and resources; creates and maintains information linkages to related artifacts. Systems Thinking - Defines the system of interest by drawing the boundaries, identifying its context within its environment, its interfaces, and that it has a lifecycle to aid in planning the problem statement, scope and deliverables ; analyzes linkages and interactions between elements that comprise the system of interest by using appropriate methods, models and integration of outcomes to understand the system, predict its behavior and devise modifications to it in order to produce the desired effects. Collaborates - Building partnerships and working collaboratively with others to meet shared objectives. Communicates effectively - Developing and delivering multi-mode communications that convey a clear understanding of the unique needs of different audiences. Decision quality - Making good and timely decisions that keep the organization moving forward. Drives results - Consistently achieving results, even under tough circumstances. Self-development - Actively seeking new ways to grow and be challenged using both formal and informal development channels. Values differences - Recognizing the value that different perspectives and cultures bring to an organization. Applies Principles of Statistical Methods - Analyzes technical data using descriptive statistics, probability distributions, graphical analysis, and statistical inference (population and sample, confidence intervals, and hypothesis testing); models relationships between response and independent variables using analysis of variance, regression, and design of experiments to make rigorous, data-based decisions. Technical Documentation - Documents information based on knowledge gained as part of technical function activities; communicates to stakeholders with the goal of enabling improved technical productivity and effective knowledge transfer to others who were not originally part of the initial learning. Product Development Execution, Monitoring and Control - Plans, schedules, coordinates and executes the activities involved in developing a product to a respectively aligned hierarchy of requirements and technical profiles; monitors and communicates across functional boundaries to meet project resource and quality expectations; ensures product capability meets or exceeds expectations and takes mitigating actions when project risks are higher than expected; understands the full product life cycle process and stakeholders. Design and Application of Open/Closed Loop Controls - Specifies software features that interact with mechanical, hydraulic, chemical and electronic systems to deliver desired system states; specifies control system architectures which include appropriate measurements, correct actuation, and algorithms for Cummins' products; configures and/or understands open/closed loop feedback controls features and the system interactions between hardware and software in Cummins' products. Education, Licenses, Certifications: College, university, or equivalent Bachelor's degree in Engineering or appropriate STEM field is required. Post-graduate (Master's) degree relevant to this discipline area may be required for select roles. This position may require licensing for compliance with export controls or sanctions regulations. Experience: Entry level/Early career professional. Preferred candidates would have relevant experience working in either a temporary student employment environment (intern, co-op, or other extracurricular team activities) or as an early career professional in a relevant technical discipline area. Knowledge of MS Office tools is also preferred Additional Responsibilities Unique to this Position Tool and Function Skills : knowledge of Engine, Aftertreatment or System simulation using either GT, AVL boost or Cruise M, Matlab/Simulink. Communication Skills : Must have good communication skills with internal and external stakeholders. Preferred Skills : Knowledge of internal combustion engine and emissions science principles. Knowledge of Engine/ Aftertreatment system and system simulation models Knowledge or working level experience with model-based development using Simulink is preferred. Awareness of Emission test cycle/ testcell data and field data analysis. Proficiency in fluid dynamics, heat transfer, and thermodynamics. Experience : Experience with 1-D systems simulations and tuning. Prior equivalent engineering work experience in a relevant discipline area with a demonstrated track record of technical problem solving and quality decision making.

: Kotak811 is a completely digital, in-your-phone, mobile bank. It was founded in 2016 to help India adapt, evolve, and eventually overcome the barrier of digital banking and make it accessible to all. Our sole aim is to bring you the excitement and ease of digital banking with the trust and reassurance of Kotak Mahindra Bank’s experience and legacy. Incubated by Kotak Mahindra Bank, Kotak811 is where innovation meets banking to offer the user the most seamless banking experience there has ever been. (Kotak 811 is a digital zero balance savings account that can be opened online by everyone (Indian residents only) which acts as a one-stop solution for all your financial needs and day to-day transactions.) Equal Employment OpportunityAll employment decisions shall be made without regard to age, race, creed, colour, religion, sex, national origin, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by the state, or local law. Kotak 811, is committed to providing equal employment opportunities RoleData Scientist 2 Key responsibilities: 1- Development of Predictions Model using ML algorithms in entire customer journey and life cycle (Acquisition, Behaviour, Collections, Propensity, Marketing, Fraud, etc.) 2- Design and Strategy Development to offer best in class Assets Products to customers. 3- Building Prediction Model using big data from digital signals, alternate data and beyond. 4- Partnering, exploring and expanding solutions with different data providers and Companies. 5- Ownership and Responsibility to lead/manage large scale projects/model Development & Deployment. Skill Sets & key requirements: 1- Advanced degree in an analytical field (e.g., Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, Data Analysis) or substantial hands on work experience in the space 2- Should have worked in Banking/Fintech domain in Credit/Fraud Risk with strong understanding of Bureau Data & in-house banking Data. 3- Advance Coding skills with at least one of these- Python/SQL/Pyspark languages 1- Strong understanding of the Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback loop. 2- Have worked across end to end in model design, development, validation, documentation & deployment. 3- Create great data stories with expertise in robust EDA and statistical inference

Data Scientist – 3 – Kotak811 Kotak811 is a Neobank incubated by Kotak Mahindra Bank, with a view of providing completely digitized banking services in the convenience of the customer’s mobile phone. 811 is an early mover in the Indian fintech space that started off as a downloadable savings bank account in 2017, post demonetization, when India took one step closer to a digital economy. The Data Scientist-3 in Bangalore (or Mumbai) will be part of the 811 Data Strategy Group that comprises Data Engineers, Data Scientists and Data Analytics professionals. He/she will be associated with one of the key functional areas such as Product Strategy, Cross Sell, Asset Risk, Fraud Risk, Customer Experience etc. and help build robust and scalable solutions that are deployed for real time or near real time consumption and integrated into our proprietary Customer Data Platform (CDP). This is an exciting opportunity to work on data driven analytical solutions and have a profound influence on the growth trajectory of a super fast evolving digital product. Key of The Role Advanced degree in an analytical field (e.g., Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, Data Analysis) or substantial hands on work experience in the space 7 - 10 Years of relevant experience in the space Expertise in mining AI/ML opportunities from open ended business problems and drive solution design/development while closely collaborating with engineering, product and business teams Strong understanding of advanced data mining techniques, curating, processing and transforming data to produce sound datasets. Strong experience in NLP, time series forecasting and recommendation engines preferred Create great data stories with expertise in robust EDA and statistical inference. Should have at least a foundational understanding in Experimentation design Strong understanding of the Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback loop. Exposure to Deep Learning applications and tools like TensorFlow, Theano, Torch, Caffe preferred Experience with analytical programming languages, tools and libraries (Python a must) as well as Shell scripting. Should be proficient in developing production ready code as per best practices. Experience in using Scala/Java/Go based libraries a big plus Very proficient is SQL and other relational databases along with PySpark or Spark SQL. Proficient is using NoSQL databases. Experience in using GraphDBs like Neo4j a plus. Candidate should be able to handle unstructured data with ease. Candidate should have experience in working with MLEs and be proficient (with experience) in using MLOps tools. Should be able to consume the capabilities of said tools with deep understanding of deployment lifecycle. Experience in CI/CD deployment is a big plus. Knowledge of key concepts in distributed systems like replication, serialization, concurrency control etc. a big plus Good understanding of programming best practices and building code artifacts for reuse. Should be comfortable with version controlling and collaborate comfortably in tools like git Ability to create frameworks that can perform model RCAs using analytical and interpretability tools. Should be able to peer review model documentations/code bases and find opportunities Experience in end-to-end delivery of AI driven Solutions (Deep learning , traditional data science projects) Strong communication, partnership and teamwork skills Should be able to guide and mentor teams while leading them by example. Should be an integral part of creating a team culture focused on driving collaboration, technical expertise and partnerships with other teams Ability to work in an extremely fast paced environment, meet deadlines, and perform at high standards with limited supervision A self-starter who is looking to build grounds up and contribute to the making of a potential big name in the space Experience in Banking and financial services is a plus. However, sound logical reasoning and first principles problem solving are even more critical A typical day in the life of the job role1. As a key partner at the table, attend key meetings with the business team to bring in the data perspective to the discussions 2. Perform comprehensive data explorations around to generate inquisitive insights and scope out the problem 3. Develop simplistic to advanced solutions to address the problem at hand. We believe in making swift (albeit sometimes marginal) impact to business KPIs and hence adopt an MVP approach to solution development 4. Build re-usable code analytical frameworks to address commonly occurring business questions 5. Perform 360-degree customer profiling and opportunity analyses to guide new product strategy. This is a nascent business and hence opportunities to guide business strategy are plenty 6. Guide team members on data science and analytics best practices to help them overcome bottlenecks and challenges 7. The role will be an approximate 60% IC – 40% leading and the ratios can vary basis need and fit 8. Develop Customer-360 Features that will be integrated into the Customer Data Platform (CDP) to enhance the single view of our customer Websitehttps://www.kotak811.com/

Key responsibilities: 1- Development of Predictions Model using ML algorithms in entire customer journey and life cycle (Acquisition, Behaviour, Collections, Propensity, Marketing, Fraud, etc.) 2- Design and Strategy Development to offer best in class Assets Products to customers. 3- Building Prediction Model using big data from digital signals, alternate data and beyond. 4- Partnering, exploring and expanding solutions with different data providers and Companies. 5- Ownership and Responsibility to lead/manage large scale projects/model Development & Deployment. Skill Sets & key requirements: 1- Advanced degree in an analytical field (e.g., Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, Data Analysis) or substantial hands on work experience in the space 2- Should have worked in Banking/Fintech domain in Credit/Fraud Risk with strong understanding of Bureau Data & in-house banking Data. 3- Advance Coding skills with at least one of these- Python/SQL/Pyspark languages 1- Strong understanding of the Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback loop. 2- Have worked across end to end in model design, development, validation, documentation & deployment. 3- Create great data stories with expertise in robust EDA and statistical inference

The Data Scientist-3 in Bangalore (or Mumbai) will be part of the 811 Data Strategy Group that comprises Data Engineers, Data Scientists and Data Analytics professionals. He/she will be associated with one of the key functional areas such as Product Strategy, Cross Sell, Asset Risk, Fraud Risk, Customer Experience etc. and help build robust and scalable solutions that are deployed for real time or near real time consumption and integrated into our proprietary Customer Data Platform (CDP). This is an exciting opportunity to work on data driven analytical solutions and have a profound influence on the growth trajectory of a super fast evolving digital product. Key Requirements of The Role Advanced degree in an analytical field (e.g., Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, Data Analysis) or substantial hands on work experience in the space 7 - 10 Years of relevant experience in the space Expertise in mining AI/ML opportunities from open ended business problems and drive solution design/development while closely collaborating with engineering, product and business teams Strong understanding of advanced data mining techniques, curating, processing and transforming data to produce sound datasets. Strong experience in NLP, time series forecasting and recommendation engines preferred Create great data stories with expertise in robust EDA and statistical inference. Should have at least a foundational understanding in Experimentation design Strong understanding of the Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback loop. Exposure to Deep Learning applications and tools like TensorFlow, Theano, Torch, Caffe preferred Experience with analytical programming languages, tools and libraries (Python a must) as well as Shell scripting. Should be proficient in developing production ready code as per best practices. Experience in using Scala/Java/Go based libraries a big plus Very proficient is SQL and other relational databases along with PySpark or Spark SQL. Proficient is using NoSQL databases. Experience in using GraphDBs like Neo4j a plus. Candidate should be able to handle unstructured data with ease. Candidate should have experience in working with MLEs and be proficient (with experience) in using MLOps tools. Should be able to consume the capabilities of said tools with deep understanding of deployment lifecycle. Experience in CI/CD deployment is a big plus. Knowledge of key concepts in distributed systems like replication, serialization, concurrency control etc. a big plus Good understanding of programming best practices and building code artifacts for reuse. Should be comfortable with version controlling and collaborate comfortably in tools like git Ability to create frameworks that can perform model RCAs using analytical and interpretability tools. Should be able to peer review model documentations/code bases and find opportunities Experience in end-to-end delivery of AI driven Solutions (Deep learning , traditional data science projects) Strong communication, partnership and teamwork skills Should be able to guide and mentor teams while leading them by example. Should be an integral part of creating a team culture focused on driving collaboration, technical expertise and partnerships with other teams Ability to work in an extremely fast paced environment, meet deadlines, and perform at high standards with limited supervision A self-starter who is looking to build grounds up and contribute to the making of a potential big name in the space Experience in Banking and financial services is a plus. However, sound logical reasoning and first principles problem solving are even more critical A typical day in the life of the job role: As a key partner at the table, attend key meetings with the business team to bring in the data perspective to the discussions Perform comprehensive data explorations around to generate inquisitive insights and scope out the problem Develop simplistic to advanced solutions to address the problem at hand. We believe in making swift (albeit sometimes marginal) impact to business KPIs and hence adopt an MVP approach to solution development Build re-usable code analytical frameworks to address commonly occurring business questions Perform 360-degree customer profiling and opportunity analyses to guide new product strategy. This is a nascent business and hence opportunities to guide business strategy are plenty Guide team members on data science and analytics best practices to help them overcome bottlenecks and challenges The role will be an approximate 60% IC 40% leading and the ratios can vary basis need and fit Develop Customer-360 Features that will be integrated into the Customer Data Platform (CDP) to enhance the single view of our customer

The Data Scientist-3 in Bangalore (or Mumbai) will be part of the 811 Data Strategy Group that comprises Data Engineers, Data Scientists and Data Analytics professionals He/she will be associated with one of the key functional areas such as Product Strategy, Cross Sell, Asset Risk, Fraud Risk, Customer Experience etc and help build robust and scalable solutions that are deployed for real time or near real time consumption and integrated into our proprietary Customer Data Platform (CDP) This is an exciting opportunity to work on data driven analytical solutions and have a profound influence on the growth trajectory of a super fast evolving digital product Key Requirements of The Role Advanced degree in an analytical field (eg, Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, Data Analysis) or substantial hands on work experience in the space 7 - 10 Years of relevant experience in the space Expertise in mining AI/ML opportunities from open ended business problems and drive solution design/development while closely collaborating with engineering, product and business teams Strong understanding of advanced data mining techniques, curating, processing and transforming data to produce sound datasets Strong experience in NLP, time series forecasting and recommendation engines preferred Create great data stories with expertise in robust EDA and statistical inference Should have at least a foundational understanding in Experimentation design Strong understanding of the Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback loop Exposure to Deep Learning applications and tools like TensorFlow, Theano, Torch, Caffe preferred Experience with analytical programming languages, tools and libraries (Python a must) as well as Shell scripting Should be proficient in developing production ready code as per best practices Experience in using Scala/Java/Go based libraries a big plus Very proficient is SQL and other relational databases along with PySpark or Spark SQL Proficient is using NoSQL databases Experience in using GraphDBs like Neo4j a plus Candidate should be able to handle unstructured data with ease Candidate should have experience in working with MLEs and be proficient (with experience) in using MLOps tools Should be able to consume the capabilities of said tools with deep understanding of deployment lifecycle Experience in CI/CD deployment is a big plus Knowledge of key concepts in distributed systems like replication, serialization, concurrency control etc a big plus Good understanding of programming best practices and building code artifacts for reuse Should be comfortable with version controlling and collaborate comfortably in tools like git Ability to create frameworks that can perform model RCAs using analytical and interpretability tools Should be able to peer review model documentations/code bases and find opportunities Experience in end-to-end delivery of AI driven Solutions (Deep learning , traditional data science projects) Strong communication, partnership and teamwork skills Should be able to guide and mentor teams while leading them by example Should be an integral part of creating a team culture focused on driving collaboration, technical expertise and partnerships with other teams Ability to work in an extremely fast paced environment, meet deadlines, and perform at high standards with limited supervision A self-starter who is looking to build grounds up and contribute to the making of a potential big name in the space Experience in Banking and financial services is a plus However, sound logical reasoning and first principles problem solving are even more critical A typical day in the life of the job role: As a key partner at the table, attend key meetings with the business team to bring in the data perspective to the discussions Perform comprehensive data explorations around to generate inquisitive insights and scope out the problem Develop simplistic to advanced solutions to address the problem at hand. We believe in making swift (albeit sometimes marginal) impact to business KPIs and hence adopt an MVP approach to solution development Build re-usable code analytical frameworks to address commonly occurring business questions Perform 360-degree customer profiling and opportunity analyses to guide new product strategy. This is a nascent business and hence opportunities to guide business strategy are plenty Guide team members on data science and analytics best practices to help them overcome bottlenecks and challenges The role will be an approximate 60% IC 40% leading and the ratios can vary basis need and fit Develop Customer-360 Features that will be integrated into the Customer Data Platform (CDP) to enhance the single view of our customer

Role: Data Scientist 2 Key responsibilities: 1- Development of Predictions Model using ML algorithms in entire customer journey and life cycle (Acquisition, Behaviour, Collections, Propensity, Marketing, Fraud, etc.) 2- Design and Strategy Development to offer best in class Assets Products to customers. 3- Building Prediction Model using big data from digital signals, alternate data and beyond. 4- Partnering, exploring and expanding solutions with different data providers and Companies. 5- Ownership and Responsibility to lead/manage large scale projects/model Development & Deployment. Skill Sets & key requirements: 1- Advanced degree in an analytical field (e.g., Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, Data Analysis) or substantial hands on work experience in the space 2- Should have worked in Banking/Fintech domain in Credit/Fraud Risk with strong understanding of Bureau Data & in-house banking Data. 3- Advance Coding skills with at least one of these- Python/SQL/Pyspark languages 4- Strong understanding of the Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback loop. 5- Have worked across end to end in model design, development, validation, documentation & deployment. 6- Create great data stories with expertise in robust EDA and statistical inference

Data Scientist 2-Digital Banking Kotak 811-Regional Sales Role: Data Scientist 2 Key responsibilities: 1- Development of Predictions Model using ML algorithms in entire customer journey and life cycle (Acquisition, Behaviour, Collections, Propensity, Marketing, Fraud, etc.) 2- Design and Strategy Development to offer best in class Assets Products to customers. 3- Building Prediction Model using big data from digital signals, alternate data and beyond. 4- Partnering, exploring and expanding solutions with different data providers and Companies. 5- Ownership and Responsibility to lead/manage large scale projects/model Development & Deployment. Skill Sets & key requirements: 1- Advanced degree in an analytical field (e.g., Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, Data Analysis) or substantial hands on work experience in the space 2- Should have worked in Banking/Fintech domain in Credit/Fraud Risk with strong understanding of Bureau Data & in-house banking Data. 3- Advance Coding skills with at least one of these- Python/SQL/Pyspark languages 4- Strong understanding of the Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback loop. 5- Have worked across end to end in model design, development, validation, documentation & deployment. 6- Create great data stories with expertise in robust EDA and statistical inference

The Data Scientist-3 in Bangalore (or Mumbai) will be part of the 811 Data Strategy Group that comprises Data Engineers, Data Scientists and Data Analytics professionals. He/she will be associated with one of the key functional areas such as Product Strategy, Cross Sell, Asset Risk, Fraud Risk, Customer Experience etc. and help build robust and scalable solutions that are deployed for real time or near real time consumption and integrated into our proprietary Customer Data Platform (CDP). This is an exciting opportunity to work on data driven analytical solutions and have a profound influence on the growth trajectory of a super fast evolving digital product. Key Requirements of The Role Advanced degree in an analytical field (e.g., Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, Data Analysis) or substantial hands on work experience in the space 7 - 10 Years of relevant experience in the space Expertise in mining AI/ML opportunities from open ended business problems and drive solution design/development while closely collaborating with engineering, product and business teams Strong understanding of advanced data mining techniques, curating, processing and transforming data to produce sound datasets. Strong experience in NLP, time series forecasting and recommendation engines preferred Create great data stories with expertise in robust EDA and statistical inference. Should have at least a foundational understanding in Experimentation design Strong understanding of the Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback loop. Exposure to Deep Learning applications and tools like TensorFlow, Theano, Torch, Caffe preferred Experience with analytical programming languages, tools and libraries (Python a must) as well as Shell scripting. Should be proficient in developing production ready code as per best practices. Experience in using Scala/Java/Go based libraries a big plus Very proficient is SQL and other relational databases along with PySpark or Spark SQL. Proficient is using NoSQL databases. Experience in using GraphDBs like Neo4j a plus. Candidate should be able to handle unstructured data with ease. Candidate should have experience in working with MLEs and be proficient (with experience) in using MLOps tools. Should be able to consume the capabilities of said tools with deep understanding of deployment lifecycle. Experience in CI/CD deployment is a big plus. Knowledge of key concepts in distributed systems like replication, serialization, concurrency control etc. a big plus Good understanding of programming best practices and building code artifacts for reuse. Should be comfortable with version controlling and collaborate comfortably in tools like git Ability to create frameworks that can perform model RCAs using analytical and interpretability tools. Should be able to peer review model documentations/code bases and find opportunities Experience in end-to-end delivery of AI driven Solutions (Deep learning , traditional data science projects) Strong communication, partnership and teamwork skills Should be able to guide and mentor teams while leading them by example. Should be an integral part of creating a team culture focused on driving collaboration, technical expertise and partnerships with other teams Ability to work in an extremely fast paced environment, meet deadlines, and perform at high standards with limited supervision A self-starter who is looking to build grounds up and contribute to the making of a potential big name in the space Experience in Banking and financial services is a plus. However, sound logical reasoning and first principles problem solving are even more critical A typical day in the life of the job role: 1. As a key partner at the table, attend key meetings with the business team to bring in the data perspective to the discussions 2. Perform comprehensive data explorations around to generate inquisitive insights and scope out the problem 3. Develop simplistic to advanced solutions to address the problem at hand. We believe in making swift (albeit sometimes marginal) impact to business KPIs and hence adopt an MVP approach to solution development 4. Build re-usable code analytical frameworks to address commonly occurring business questions 5. Perform 360-degree customer profiling and opportunity analyses to guide new product strategy. This is a nascent business and hence opportunities to guide business strategy are plenty 6. Guide team members on data science and analytics best practices to help them overcome bottlenecks and challenges 7. The role will be an approximate 60% IC 40% leading and the ratios can vary basis need and fit 8. Develop Customer-360 Features that will be integrated into the Customer Data Platform (CDP)

Capgemini Invent Capgemini Invent is the digital innovation, consulting and transformation brand of the Capgemini Group, a global business line that combines market leading expertise in strategy, technology, data science and creative design, to help CxOs envision and build whats next for their businesses. Your role Solution Design:Collaborate with stakeholders to design and implement effective Generative AI solutions tailored to specific business requirements, ensuring alignment with industry best practices. Framework Development:Build and enhance frameworks for deploying Generative AI applications, ensuring they are robust, scalable, and optimized for hyper-scaler environments. LLM Integration:Work extensively with Large Language Models, optimizing their performance for various applications and use cases, while leveraging the capabilities of hyper-scaler platforms. Deep learning and ML expertiseExcellent understanding and hand-on experience of data-science and machine learning techniques & algorithms for supervised & unsupervised problems, NLP and computer vision. Good applied statistics skills, such as distributions, statistical inference & testing, etc. Hands-On Development:Engage in hands-on coding and development of AI models and algorithms, utilizing the latest tools and technologies in the Generative AI landscape. Proof of Concept (PoC) Development:Lead the creation of PoCs that demonstrate the capabilities of Generative AI solutions. Your Profile Education:Bachelors degree in Computer Science, Software Engineering, or a related field; Masters degree is a plus. Experience:Hands-on experience in AI/ML development, with a strong emphasis on Generative AI technologies in hyper-scaler environments Technical Skills: Proficiency in programming languages such as Python, TensorFlow, or PyTorch; experience with LLM frameworks and model deployment on platforms like AWS, Azure, or Google Cloud. What you will love about working here We recognize the significance of flexible work arrangements to provide support. Be it remote work, or flexible work hours, you will get an environment to maintain healthy work life balance. At the heart of our mission is your career growth. Our array of career growth programs and diverse professions are crafted to support you in exploring a world of opportunities. Equip yourself with valuable certifications in the latest technologies such as Generative AI. About Capgemini Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, cloud and data, combined with its deep industry expertise and partner ecosystem. The Group reported 2023 global revenues of 22.5 billion.