20 Statistical Modelling Jobs

As Data Engineer at IBM you will harness the power of data to unveil captivating stories and intricate patterns. Youll contribute to data gathering, storage, and both batch and real-time processing. Collaborating closely with diverse teams, youll play an important role in deciding the most suitable data management systems and identifying the crucial data required for insightful analysis. As a Data Engineer, youll tackle obstacles related to database integration and untangle complex, unstructured data sets. In this role, your responsibilities may include: Implementing and validating predictive models as well as creating and maintain statistical models with a focus on big data, incorporating a variety of statistical and machine learning techniques Designing and implementing various enterprise search applications such as Elasticsearch and Splunk for client requirements Work in an Agile, collaborative environment, partnering with other scientists, engineers, consultants and database administrators of all backgrounds and disciplines to bring analytical rigor and statistical methods to the challenges of predicting behaviours. Build teams or writing programs to cleanse and integrate data in an efficient and reusable manner, developing predictive or prescriptive models, and evaluating modelling results Required education Bachelor's Degree Preferred education Master's Degree Required technical and professional expertise Experience in the integration efforts between Alation and Manta, ensuring seamless data flow and compatibility. Collaborate with cross-functional teams to gather requirements and design solutions that leverage both Alation and Manta platforms effectively.. Develop and maintain data governance processes and standards within Alation, leveraging Manta's data lineage capabilities.. Analyze data lineage and metadata to provide insights into data quality, compliance, and usage patterns Preferred technical and professional experience Lead the evaluation and implementation of new features and updates for both Alation and Manta platforms Ensuring alignment with organizational goals and objectives. Drive continuous improvement initiatives to enhance the efficiency and effectiveness of data management processes, leveraging Alati

We seek a Digital Marketing Specialist, Mid Level with Marketing Media Mix who shares our passion for innovation and change. This role is critical to helping our business partners evolve and adapt to consumers' personalized expectations in this new technological era. Roles and Responsibilities: Delivery of MMM/ROI projects: Delivery of multiple High Impact ROI projects as described below: Data preparation, harmonization and transformation for model feed Thorough understanding of different types of Media, sales and equity datatypes Media ROI modelling with granular insights at Campaign level, Objectives, Formats and Ad types Promo ROI modelling at granular and retailer level with deep dive into various promotion techniques Sales and Equity ROI modelling at channel, format level with deep dive into marketing drivers. Ecomm. ROI projects with deep dive into retail media, influencer spends and online promotions along with other driver Develop and implement advanced market mix models to quantify the ROI of marketing activities across various product categories and channels. Interpret and analyse model outputs, identifying key trends and actionable insights for marketing optimization. Foster a collaborative and results-oriented team environment Technical Skills and Project experience requirements: Should have executed ROI / MMM projects in past. Understanding of statistical modelling techniques, including ensemble modelling, Bayesian HLM analysis and multivariate analysis. Understanding impact of product propositions and marketing drivers on brand equity metrics and deriving incremental lift from the drivers to uplift the brand sales. Understanding of data types used in MMM including sales, equity, macro-economic data Experience with data transformation techniques including nonlinear transformation, ad stock transformation of variables used in the modelling. . Experience in Ecomm. Modelling with deep dive on retail media, influencer and online promotions. Education: Bachelor s degree in engineering/Statistics or Specialization in Business analytics Prior work ex- Minimum 1 years of relevant experience This job can be filled in Bangalore/Pune #LI-Hybrid

Job Title: Data Scientist(5 Positions)/ Lead OR Manager -Data Scientist (3 positions) Experience: Data scientist (8-10 years) / Lead Data scientist(14+ years) Job Location: Whitefield, Bangalore Mode of working: Hybrid Interview Process: First Round: L1-Internal interview Second Round: Assessment shared by us needs to be completed in 48 hours Third Round: Client discussion over the submitted assessment. Final Round: HR Discussion Preferred Domain: Healthcare Insurance/ Insurance agencies / Health Insurance / Any Insurance We are looking for a talented Data Scientist to join our growing team. In this role, you will lead efforts to develop, enhance, and optimize advanced AI and machine learning models with a particular focus on Generative AI, Large Language Models (LLMs), Langchain, and Prompt Engineering. You will oversee the application of statistical modeling techniques to derive insights, build models, and lead research initiatives that push the boundaries of AI technologies. Key Responsibilities: Leadership & Collaboration: Lead a team of data scientists, researchers, and engineers working on high-impact projects related to generative models, NLP, and statistical modeling. Collaborate with cross-functional teams, including engineering, product management, and research, to deliver AI-powered products and solutions. Generative AI Development: Spearhead the development and deployment of Generative AI models and algorithms to address complex problems in areas like content generation, conversational AI, and creative automation. LLM Implementation & Optimization: Develop, fine-tune, and optimize large language models (LLMs) for diverse applications, ensuring they are robust, scalable, and accurate in real-world scenarios. Langchain Integration: Design and integrate Langchain for managing and deploying sophisticated language models with a focus on complex workflows, multi-agent systems, and real-time applications. Prompt Engineering: Lead prompt engineering efforts to optimize AI models' output quality, improve interactions, and enable more effective natural language understanding across a variety of use cases. Statistical Modeling: Utilize advanced statistical techniques to analyze and interpret data, build predictive models, and solve business-critical challenges through data-driven insights. Research & Innovation: Stay ahead of trends in AI and ML, particularly in the fields of NLP, LLMs, and generative models. Drive innovation by exploring cutting-edge techniques and methodologies in the AI space . Mentorship & Knowledge Sharing: Mentor junior team members and promote a collaborative, learning-oriented environment. Share knowledge and foster an atmosphere of continuous improvement within the data science team. Performance Optimization: Ensure model performance meets or exceeds company and client expectations by identifying areas of improvement, testing new methods, and scaling the systems accordingly. Ethical AI Development: Advocate for and implement ethical considerations in the development and deployment of AI models, including fairness, transparency, and privacy. Qualifications: Required: Education: Ph.D. or Masters degree in Computer Science, Data Science, Mathematics, Statistics, or related field, or equivalent practical experience. Experience: 8+ years of experience in data science, with at least 2-3 years in a leadership role. Proven expertise in Generative AI, particularly in areas like content generation, deep learning, and language modeling. Strong background in Large Language Models (LLMs) such as GPT, T5, BERT, or similar architectures. Hands-on experience with Langchain for building NLP workflows, pipelines, and integrating external systems with LLMs. Hands-on experience of Prompt Engineering, including techniques to refine and optimize outputs for various NLP tasks. Expertise in statistical modeling and quantitative analysis, with the ability to apply techniques to solve real-world problems. Preferred: Experience working with transformer models and fine-tuning LLMs for specific tasks. Expertise in AI model evaluation and metrics (e.g., BLEU, ROUGE, perplexity). Background in developing AI-driven products from concept to deployment. Strong publication record in AI research, particularly in NLP and machine learning . Used cases( Any of them) Automated Underwriting. Customer experience enhancement. Fraud detection . Predictive analytics. Accelerated claims processing. Risk assessment and premium calculation . Customer profiling . c ustomer segmentation . Credit Risk Assessment . Personalised marketing . Anti-Money Laundering (AML) . Personalized patient care. Medical training and simulations. Medical Data Analysis. Please share your updated resume at renuka.rathi@puresoftware.com

Your day at NTT DATA The Senior Data Scientist is an advanced subject matter expert, tasked with taking accountability in the adoption of data science and analytics within the organization. The primary responsibility of this role is to participate in the creation and delivery of data-driven solutions that add business value using statistical models, machine learning algorithms, data mining, and visualization techniques. What youll be doing Key Responsibilities: Designs, develops, and programs methods, processes, and systems to consolidate and analyze unstructured, diverse big data sources to generate actionable insights and solutions for client services and product enhancement. Designs and enhances data collection procedures to include information that is relevant for building analytic systems. Responsible for ensuring that data used for analysis is processed, cleaned and, integrally verified and build algorithms necessary to find meaningful answers. Designs and codes software programs, algorithms, and automated processes to cleanse, integrate and evaluate large datasets from multiple disparate sources Provides meaningful insights from large data and metadata sources; interprets and communicates insights and findings from analysis and experiments to product, service, and business managers. Directs scalable and highly available applications leveraging the latest tools and technologies. Accountable for creatively visualizing and effectively communicating results of data analysis, insights, and ideas in a variety of formats to key decision-makers within the business. Creates SQL queries for the analysis of data and visualizes the output of the models. Responsible for ensuring that industry standards best practices are applied to development activities. Knowledge and Attributes: Advanced understanding of data modelling, statistical methods and machine learning techniques. Strong ability to thrive in a dynamic, fast-paced environment. Strong quantitative and qualitative analysis skills. Desire to acquire more knowledge to keep up to speed with the ever-evolving field of data science. Curiosity to sift through data to find answers and more insights. Advanced understanding of the information technology industry within a matrixed organization and the typical business problems such organizations face. Strong ability to translate technical findings clearly and fluently to non-technical team business stakeholders to enable informed decision-making. Strong ability to create a storyline around the data to make it easy to interpret and understand. Self-driven and able to work independently yet acts as a team player. Academic Qualifications and Certifications: Bachelors degree or equivalent in Data Science, Business Analytics, Mathematics, Economics, Engineering, Computer Science or a related field. Relevant programming certification preferred. Agile certification preferred. Required Experience: Advanced demonstrated experience in a data science position in a corporate environment and/or related industry. Advanced demonstrated experience in statistical modelling and data modelling, machine learning, data mining, unstructured data analytics, natural language processing. Advanced demonstrated experience in programming languages (R, Python, etc.). Advanced demonstrated experience working with and creating data architectures. Advanced demonstrated experience with extracting, cleaning, and transforming data and working with data owners to understand the data. Advanced demonstrated experience visualizing and/or presenting data for stakeholder use and reuse across the business.

Roles & Responsibilities Displays structured problem solving, application of right tools & techniques to solve open ended problems, Creates productized analytics solutions or frameworks independently. Understand Business & product problems and come up with deep data backed solutions. Root cause analysis and Deep dive analysis of certain product problems Running and maintaining the reporting system, presenting insights at a weekly forum Building templates, dashboards in Excel or on the intranet for operational and management reporting Data extraction as per business team request for Ad hoc analysis Business analysis and understanding Evaluating metrics to be tracked as per business goals, exploring other available metrics for deeper understanding of product performance Mentor Data analysts on day to day analysis and upskilling Work in a fast-moving environment, across multiple projects with varying levels of complexity and detailing Skills and Experience: 3-5 years of experience working with large datasets and conducting quantitative analysis. Strong understanding of statistics and prior experience in building statistical models, including hypothesis testing, product experimentation, A/B testing, and regressions. Expertise in SQL and advanced proficiency in Python for data manipulation and analysis. Previous experience working with e-commerce funnels and retention analysis. Applied knowledge of widely used analytics techniques, as well as emerging or niche techniques. Ability to conceptualize and analyze product and business metrics to assess and optimize product features. Knowledge of business modeling and basic financial metrics (bonus). Quick learner with the ability to adapt to a dynamic work environment. Team player with strong interpersonal skills and comfort in collaborating with cross-disciplinary teams. Proven expertise in designing and building dashboards, identifying key metrics, and ensuring intuitive layouts; adept at using tools like UDP and Power BI following best practices. Bachelor s degree in Computer Science, Engineering, or equivalent from a reputed institution.

Responsibilities: Collaborate with business stakeholders to understand data requirements and objectives. Analyse complex data sets to extract valuable insights and trends. Interpret data to identify opportunities for process improvements and business growth. Develop reports, dashboards, and visualisations to communicate findings effectively. Conduct data quality assessments and ensure data integrity across systems. Support data-driven decision-making by providing actionable recommendations. Work with cross-functional teams to implement data solutions that meet business needs. Stay informed on industry trends and best practices in data analysis and business intelligence Qualifications: Bachelor's degree in Business Administration, Statistics, Computer Science, or related field. Proven experience as a Business Analyst or Data Analyst in a corporate setting. Proficiency in data analysis tools (e.g., SQL, Excel, Amazon QuickSight) and database querying. Strong analytical skills with the ability to translate complex data into actionable insights. Excellent communication and presentation skills. Experience with data visualization tools and techniques. Knowledge of business processes and operations. Preferred Skills: Familiarity with ETL processes and data warehousing concepts. Experience with predictive analytics and statistical modelling. Certification in business analysis or related field is a plus. Knowledge of Agile methodologies for project management.

Exp in SAS, SQL and large amounts of data US Stakeholder exp Exp of acquisition/account management credit risk models, transactional fraud models, marketing models, collections models, finance models, loss models, Loss forecasting (PD/LGD/EAD/CECL) Required Candidate profile SAS SQL Python Credit risk Credit Card Statistical Modelling Predictive Modelling

Required Skills (Technical):- Advanced knowledge of statistical techniques, NLP, machine learning algorithms and deep learning frameworks like TensorFlow, Theano, Keras, Pytorch. Knowledge of work with Pyspark is a must. (Working with Azure Databricks platform is preferred) Advanced knowledge in handling Time Series Data and modelling it with time series algorithms. Knowledge of working with Geospatial data would be preferable. Proficiency with modern statistical modelling (regression, boosting trees, random forests, etc.), machine learning (text mining, neural network, NLP, etc.), optimization (linear optimization, nonlinear optimization, stochastic optimization, etc.) methodologies. Build complex predictive models using ML and DL techniques with production quality code and jointly own complex data science workflows with the Data Engineering team. Familiar with modern data analytics architecture and data engineering technologies (SQL and No-SQL databases). Knowledge of REST APIs and Web Services 6. Experience with Python, R, sh/bash Required Skills (Non-Technical):- Fluent in English Communication (Spoken and verbal) Should be a team player Should have a learning aptitude Detail-oriented, analytically. Extremely organized with strong time-management skills Problem Solving & Critical Thinking Require Work Location :Remote , hyderabad,ahmedabad,pune,chennai,kolkata. Work Timing : 2:30 PM to 11:30 PM IST

Requirements 6-10 yrs of Strong experience with SAP UDF configuration and functional setup. Knowledge of statistical modelling concepts used in forecasting. Familiarity with integration to SAP CAR (Customer Activity Repository), F&R, or other planning tools. Good communication and documentation skills. Experience with large-scale retail forecasting systems is a plus. Responsibilities Gather business requirements and translate them into functional specifications for Unified Demand Forecasting. Configure and support SAP UDF modules including demand modelling, forecasting profiles, and integration with planning systems. Analyse demand signals and improve forecast accuracy through system tuning and configuration. Collaborate with business stakeholders, data science teams, and SAP technical teams. Conduct testing, support UAT, and document solutions. Monitor forecast performance and implement improvements as needed.

Data Science professional with a proven track record in training Engineering, IT, Diploma, Polytechnic and Technical candidates. With over a 7 yrs of experience in Artificial Intelligence, Machine Learning, Big Data, and Cloud Computing, Specialise in delivering industry-oriented, hands-on training that equips candidates with the technical proficiency required in today's data-driven world.

Key responsibilities Work closely with the broader team do define and execute EDA road map Integrated solution for business based on data driven insights and strategies. Perform advance Statistical and Machine learning modelling exercise to develop descriptive/ predictive/ prescriptive models Perform data visualization and reporting for effective communication of insights generated by modelling exercises Develop basic database queries for data extraction Ability to communicate models and analysis in a clear and precise manner. Promote new learning and technique in the field of Data Science and its application in Insurance. Perform peer reviews and ideation sessions. Experience & Qualifications Bachelor and/or Masters in Statistics/Economics/Operations Research from Tier 1 colleges. Experience of 7-10 years of data science experience with alteast 4-6 years of experience in life insurance experience in model development, validation, and implementation with atleast 4 years of Insurance Experience Hands-on experience with R and/or Python for hypothesis testing, statistical modelling and Machine learning. Actuarial Exams is a plus Proven track record on algorithm development for business problems. Understanding of common algorithms like GLM, Decision Trees, Random Forests, XGBoost and Time Series Excellent communication and interpersonal skills, with the ability to explain complex algorithms to nontechnical stakeholders.

Job Description: Pratt & Whitney is seeking data science & analytics engineer in our Data science, Analytics & Methods team at the Pratt & Whitney India Engineering Center (IEC) in Bengaluru India. The role will span working with multi-disciplinary organizations including system level, module level and component level across the product life cycle. Main responsibilities include providing insights from existing engine data and delivering data-based decisions using forecasting models that can be embedded in our current workflows. Key Responsibilities: Work with engineering organization leads to Leverage data collected on development and fielded engines to provide insights and better decision making Identify gaps in current data-based processes and tools and provide direction for tool development team Define, prototype, test, deploy and monitor statistical models to provide data-based predictions and forecasts Understand and recommend appropriate Data Analytics methods and software solutions Analysis and Development to be performed on Quality Nonconformance (QN) data in order to establish, opportunities, trends and forecasts Prepare and maintain documentation of forecasting methods and tools Collaborate with other methods and tool team members and stakeholders to identify continuous improvement opportunities Works autonomously with limited oversight & may provide guidance to lower experienced professionals with tasks and assignments. Exhibit the ownership for the defined milestones in terms of schedules & quality of deliverables Required Qualifications: Bachelors degree (with 4+ year experience) or Advanced degree (with 2+ year experience) in Data Science, Mathematics, Engineering or Statistics. 2+ years of experience in SQL. 2+ years of experience building machine learning/statistical models. Preferred Qualifications: Experience in Aerospace domain. Experience developing in Python. Experience developing PowerBI/Qlik dashboards. Experience with data & text mining. Experience with Amazon Web Services (AWS). Experience working in an agile environment. Good communication skills and working as a team player.

Leading Financial Company Hiring Portfolio Risk Analytics (SAS/Python/R, SQL) Bank/NBFC/ Fintech (PUNE) Interested please call MAHEK 9137263337 and drop CV at quotientconsultancy@gmail.com Job Purpose As a Portfolio Risk Analyst, you will play a critical role in assessing and monitoring credit risk of the loan portfolio. Your primary responsibility will be to analyze the companys loan portfolio, evaluate risk exposures and generate insights on portfolio risk. You will adopt a data driven approach, leveraging data analytics tools and statistical techniques to provide valuable insights on portfolio behaviour and present your findings effectively to the senior management to support informed decision-making and credit risk management. . Principal Accountabilities Loan Portfolio Analysis: Conduct periodic portfolio analysis including bounce & delinquency trends, generate and analyze portfolio cuts, perform static pool analysis etc. Risk Modeling and Reporting: Develop and maintain credit risk models, perform validation of existing risk models, generate reports summarizing risk trends, perform early delinquency and pre-delinquency analysis. Create and deploy customer segmentation models and risk ranking models. Data Analytics: Utilize data analytics tools (such as SQL or Python) to extract insights from raw data. Identify patterns, trends, and anomalies within the portfolio. Apply statistical techniques to quantify risk. Present the insights using data visualization tools. Market Insights: Stay up to date with developments in the financial services sector including the regulatory landscape. Keep track of the technological advances being made in areas such as AI and develop an understanding of various use cases of such technologies for the companys business. Cross-functional Collaboration: Collaborate with various departments, including Finance, IT and Data Analytics from time to time to gain better understanding of data and underlying processes. Ad-hoc assignments: Ad-hoc assignments related to credit risk and portfolio analytics from time to time. Desired Profile Minimum Qualification – Master’s degree in data science, IT, Finance, MBA Strong expertise in SAS/Python/R, SQL Strong understanding of statistical modelling and data visualization tools Minimum 5 years of experience in retail lending space in credit risk role at a Bank / Large NBFC / Fintech / Reputed Consultancy Firm; experience in portfolio risk analytics and system automation. Effective communication and presentation skills, stakeholder management capabilities, inclination towards automation

Key responsibilities Work closely with the broader team do define and execute EDA road map Integrated solution for business based on data driven insights and strategies. Perform advance Statistical and Machine learning modelling exercise to develop descriptive/ predictive/ prescriptive models Perform data visualization and reporting for effective communication of insights generated by modelling exercises Develop basic database queries for data extraction Ability to communicate models and analysis in a clear and precise manner. Promote new learning and technique in the field of Data Science and its application in Insurance. Perform peer reviews and ideation sessions. Experience & Qualifications Bachelor and/or Masters in Statistics/Economics/Operations Research from Tier 1 colleges. Experience of 7-10 years of data science experience with alteast 4-6 years of experience in life insurance experience in model development, validation, and implementation with atleast 4 years of Insurance Experience Hands-on experience with R and/or Python for hypothesis testing, statistical modelling and Machine learning. Actuarial Exams is a plus Proven track record on algorithm development for business problems. Understanding of common algorithms like GLM, Decision Trees, Random Forests, XGBoost and Time Series Excellent communication and interpersonal skills, with the ability to explain complex algorithms to nontechnical stakeholders.

Opportunity with A financial platform for the informal sector, offering solutions tailored to customer's goals. As the Head of Credit Analytics, you will be responsible for defining and owning the end-to-end strategy for our credit analytics business. You will manage the credit team and has expert knowledge on credit scoring using ML, risk management strategies, portfolio analytics, debt collection and fraud detection. Key responsibilities: - Lead the credit analytics team comprising data analysts and data scientists - Create competitive advantage for business through analytics and machine learning - Co-own business targets for analytics driven business outcomes - Create new age solutions through usage and implement ML solutions in big data environment - Bring in best practices to develop statistical and/ or machine learning techniques to build models that address business needs. - Collaborate with the team to improve the effectiveness of business decisions using data and machine learning/predictive modelling. - Utilize effective project planning techniques to break down complex projects into tasks and ensure deadlines are kept. - Communicate findings to team and to executives and other stake holders to ensure models are well understood and incorporated into business processes. Skills: - 7+ years in advanced analytics, statistical modelling, and machine learning with 4+ years in credit Best practice knowledge in credit risk - strong understanding of the full lifecycle from origination to debt collection. - Well versed with ML algos, BIG data concepts and cloud implementations - Experience of implementing real-time decision analytics solutions - Proven track record of developing robust and insightful communications suitable for senior management, our regulators and the Board - Excellent communication skills. Delves into a complex problem and solves it by using large amounts of data. But can simplify as appropriate to cut to the core issue and present findings - Great leadership skills. Very proactive. Interacts with other business leaders to both learn as well as influence. Partnership approach and collaborative attitude - Analytical thinker and well-versed in dealing with numbers and large quantities of data CFA/FRM is a plus.

Data Science professional with a proven track record in training Engineering, IT, Diploma, Polytechnic and Technical candidates. With over a 7 yrs of experience in Artificial Intelligence, Machine Learning, Big Data, and Cloud Computing, Specialise in delivering industry-oriented, hands-on training that equips candidates with the technical proficiency required in today's data-driven world.

Data Science professional with a proven track record in training Engineering, IT, Diploma, Polytechnic and Technical candidates. With over a 7 yrs of experience in Artificial Intelligence, Machine Learning, Big Data, and Cloud Computing, Specialise in delivering industry-oriented, hands-on training that equips candidates with the technical proficiency required in today's data-driven world. Experienced Data Science professionals with a proven track record in training BSc. M.Sc. in Mathematics, Statistics, or Computer Science Keywords : - Statistical Modelling, Machine Learning, Deep Learning,Hadoop, Spark, Apache Kafka, Python, Scala Mandatory Key Skills : - Data Science Techniques, Big Data Technologies, Python, R, Scala, Data Engineering, Cloud Platforms & DevOps Integration Work Experience Required : - 7 Years

Risk Data Validation & Control (RDV&C) team is responsible for quality assurance activities in relation to critical, complex and technical risks and regulatory topics that affect Deutsche Bank (DB). RDV&C are part of the Credit Risk Data Unit (CRDU) team within Group Finance and their key stakeholders include but are not limited to: Business Finance Risk Management (CRM/MRM) Group Reporting Regulatory Policy Adherence Group Production IT Support Your key responsibilities Completion of required month end Quality assurance controls and to validate variance Credit Risk RWA Exposure Analysis Leverage exposure regulatory metric Other reg metric like CVA, EC, EL, Calculation of the exposure wherever required and posting in relevant platforms Navigate through the complex algorithms built in the risk engine to perform root cause analysis on the exposure calculations. Ultimately the calculated output should reflect the economics of the portfolio. Data Quality proactively manage the investigation and resolution of month end issues on the regulatory metrics Liaising with relevant stakeholder for RCA and reporting Providing subject matter expertise and analytics to support Finance and the Risk team Presentation of the reg metric to senior audience across the globe Participation in CTB initiatives Optimisation Focus on the capital number Your skills and experience Good Knowledge of regulatory requirements like ECB CRR, CRD, Basel requirements Understanding of exposure calculation under different models e.g. SA-CCR and IMM Knowledge of Exposure Metrics like EPE/EE, Statistical Modelling (Monte Carlo Simulation etc.) An analytical mindset and good approach to problem solving Experience of process change Strong interpersonal and communication skills Organized and structured working approach Strong attention to detail Reliable team player who enjoys working in an international environment Preferred IT Skills: Python, Advance Excel(VBA), Microstrategy, MS Access

1-3 years of experience in the research and consulting (KPO) in Experience of direct interaction with clients, both through emails and conference calls Creative outlook and ability to think of multiple approaches to address a problem.Execute commodity forecasting projects for different commodities Develop understanding of commodity fundamentals through secondary research, by scanning industry reports, journals and other sources Identify price drivers and determine key quantitative variables and collect data from databases and public domain Develop statistical model (such as regression) to forecast the commodity prices Work as a lead analyst and contribute to the entire project lifecycle across assignments through exhaustive research, data interpretation, insight generation/analysis, and report preparation Deliver high-quality, client-ready output consistently and on time; ensure there are no gaps in quality pertaining to research and analysis" Qualifications Qualification : B.E,B.Tech & MCA

What You'll Do At Avalara, we are building the next generation of automated tax compliance products by using modern machine learning techniques. As part of the AI/ML team, you'll work on real-world, large-scale applications that push the boundaries of machine learning and AI. If the prospect of building impactful systems excites you, we'd love to connect! You will be reporting to Senior Manager AI/ML What Your Responsibilities Will Be Algorithm Development: Design and program integrated software algorithms to structure, analyze, and use data in both structured and unstructured environments. Statistical Modelling: Apply experimental methodologies, statistics, optimization, probability theory, and machine learning to create tools, statistical models, and algorithms using general-purpose software and statistical languages. Insights and Optimization: Develop descriptive, diagnostic, predictive, and prescriptive insights and algorithms to improve product and system performance. ML and Statistical Techniques: Use techniques such as decision trees, logistic regression, Bayesian analysis, NLP, and other methods to design algorithms that enhance product quality, data accuracy, and system performance. Code Implementation: Translate algorithms and technical specifications into efficient, production-ready code using modern programming languages like Python Debugging: Implement programming practices, perform testing and debugging, and increase system efficiency. Documentation: Create comprehensive documentation and maintenance procedures for implemented systems and algorithms. Generative AI Applications: Develop and implement generative AI technologies for applications such as content generation, conversational systems, and interactive user experiences. Emerge Technologies: Adapt machine learning methodologies to areas like artificial intelligence, image processing, and other modern technologies to promote solutions. Cloud-Based Solutions: Deploy scalable solutions in cloud-based environments (e.g., AWS, Azure, and GCP). What You'll Need To Be Successful Education: Bachelor's degree in computer science or a related field. Technical Skills: Proficiency in machine learning and natural language processing (NLP). Experience with software fundamentals, data structures, and algorithms. Hands-on experience with CI/CD pipelines, distributed systems, and cloud-based software. Expertise in generative AI and building conversational systems. Classification systems, sentiment analysis, NLP, and entity extraction Computer vision, OCR, and content extraction from images, PDFs, and HTML 7+ years of experience with custom models and ensembles Experience in cloud-based environments (e.g., AWS, Azure, and GCP) Expertise in generative AI and building conversational systems.