Job Title : Senior Data Scientist (SDS 2) Experience: 4+ years Location : Bengaluru (Hybrid) Company Overview: Akaike Technologies is a dynamic and innovative AI-driven company dedicated to building impactful solutions across various domains . Our mission is to empower businesses by harnessing the power of data and AI to drive growth, efficiency, and value. We foster a culture of collaboration , creativity, and continuous learning , where every team member is encouraged to take initiative and contribute to groundbreaking projects. We value diversity, integrity, and a strong commitment to excellence in all our endeavors. Job Description: We are seeking an experienced and highly skilled Senior Data Scientist to join our team in Bengaluru. This role focuses on driving innovative solutions using cutting-edge Classical Machine Learning, Deep Learning, and Generative AI . The ideal candidate will possess a blend of deep technical expertise , strong business acumen, effective communication skills , and a sense of ownership . During the interview, we look for a proven track record in designing, developing, and deploying scalable ML/DL solutions in a fast-paced, collaborative environment. Key Responsibilities: ML/DL Solution Development & Deployment: Design, implement, and deploy end-to-end ML/DL, GenAI solutions, writing modular, scalable, and production-ready code. Develop and implement scalable deployment pipelines using Docker and AWS services (ECR, Lambda, Step Functions). Design and implement custom models and loss functions to address data nuances and specific labeling challenges. Ability to model in different marketing scenarios of a product life cycle ( Targeting, Segmenting, Messaging, Content Recommendation, Budget optimisation, Customer scoring, risk and churn ), and data limitations(Sparse or incomplete labels, Single class learning) Large-Scale Data Handling & Processing: Efficiently handle and model billions of data points using multi-cluster data processing frameworks (e.g., Spark SQL, PySpark ). Generative AI & Large Language Models (LLMs): Leverage in-depth understanding of transformer architectures and the principles of Large and Small Language Models . Practical experience in building LLM-ready Data Management layers for large-scale structured and unstructured data . Apply foundational understanding of LLM Agents, multi-agent systems (e.g., Agent-Critique, ReACT, Agent Collaboration), advanced prompting techniques, LLM eval uation methodologies, confidence grading, and Human-in-the-Loop systems. Experimentation, Analysis & System Design: Design and conduct experiments to test hypotheses and perform Exploratory Data Analysis (EDA) aligned with business requirements. Apply system design concepts and engineering principles to create low-latency solutions capable of serving simultaneous users in real-time. Collaboration, Communication & Mentorship: Create clear solution outlines and e ffectively communicate complex technical concepts to stakeholders and team members. Mentor junior team members, providing guidance and bridging the gap between business problems and data science solutions. Work closely with cross-functional teams and clients to deliver impactful solutions. Prototyping & Impact Measurement: Comfortable with rapid prototyping and meeting high productivity expectations in a fast-paced development environment. Set up measurement pipelines to study the impact of solutions in different market scenarios. Must-Have Skills: Core Machine Learning & Deep Learning: In-depth knowledge of Artificial Neural Networks (ANN), 1D, 2D, and 3D Convolutional Neural Networks (ConvNets), LSTMs , and Transformer models. Expertise in modeling techniques such as promo mix modeling (MMM) , PU Learning , Customer Lifetime Value (CLV) , multi-dimensional time series modeling, and demand forecasting in supply chain and simulation. Strong proficiency in PU learning, single-class learning, representation learning, alongside traditional machine learning approaches. Advanced understanding and application of model explainability techniques. Data Analysis & Processing: Proficiency in Python and its data science ecosystem, including libraries like NumPy, Pandas, Dask, and PySpark for large-scale data processing and analysis. Ability to perform effective feature engineering by understanding business objectives. ML/DL Frameworks & Tools: Hands-on experience with ML/DL libraries such as Scikit-learn, TensorFlow/Keras, and PyTorch for developing and deploying models. Natural Language Processing (NLP): Expertise in traditional and advanced NLP techniques, including Transformers (BERT, T5, GPT), Word2Vec, Named Entity Recognition (NER), topic modeling, and contrastive learning. Cloud & MLOps: Experience with the AWS ML stack or equivalent cloud platforms. Proficiency in developing scalable deployment pipelines using Docker and AWS services (ECR, Lambda, Step Functions). Problem Solving & Research: Strong logical and reasoning skills. Good understanding of the Python Ecosystem and experience implementing research papers. Collaboration & Prototyping: Ability to thrive in a fast-paced development and rapid prototyping environment. Relevant to Have: Expertise in Claims data and a background in the pharmaceutical industry . Awareness of best software design practices . Understanding of backend frameworks like Flask. Knowledge of Recommender Systems, Representative learning, PU learning. Benefits and Perks: Competitive ESOP grants. Opportunity to work with Fortune 500 companies and world-class teams. Support for publishing papers and attending academic/industry conferences. Access to networking events, conferences, and seminars. Visibility across all functions at Akaike, including sales, pre-sales, lead generation, marketing, and hiring. Appendix Technical Skills (Must Haves) Having deep understanding of the following Data Processing : Wrangling : Some understanding of querying database (MySQL, PostgresDB etc), very fluent in the usage of the following libraries Pandas, Numpy, Statsmodels etc. Visualization : Exposure towards Matplotlib, Plotly, Altair etc. Machine Learning Exposure : Machine Learning Fundamentals, For ex: PCA, Correlations, Statistical Tests etc. Time Series Models, For ex: ARIMA, Prophet etc. Tree Based Models, For ex: Random Forest, XGBoost etc.. Deep Learning Models, For ex: Understanding and Experience of ConvNets, ResNets, UNets etc. GenAI Based Models : Experience utilizing large-scale language models such as GPT-4 or other open-source alternatives (such as Mistral, Llama, Claude) through prompt engineering and custom finetuning. Code Versioning Systems : Github, Git If you're interested in the job opening, please apply through the Keka link provided here: https://akaike.keka.com/careers/jobdetails/26215 Show more Show less
