Senior Analyst - Data Science

Senior Analyst - Data Science Key Responsibilities Design and implement advanced AI/ML models for business use cases, including but not limited to marketing mix modeling (MMM), churn prediction, propensity scoring, demand forecasting, and revenue/sales forecasting. Collaborate with cross-functional stakeholders product managers, data engineers, domain experts to translate business goals into AI/ML solutions. Leverage Python and modern ML/DL libraries (e.g., scikit-learn, PyTorch, statsmodels, langgraph) to process structured/unstructured data, build features, and develop scalable models. Lead end-to-end ML pipelines, from data ingestion and feature engineering to model training, evaluation, optimization, and deployment (including MLOps best practices). Conduct in-depth exploratory data analysis (EDA) and statistical investigations to extract actionable insights, validate hypotheses, and guide model development. Stay abreast of emerging ideas in AI/ML, and actively integrate ideas from research. Work across the spectrum of traditional machine learning and modern AI architectures, including the development of deep neural networks. Essential Skills Educational Background - Bachelors, Masters, or Ph.D. in a quantitative discipline such as Statistics, Mathematics, Computer Science, Data Science, AIMLor related fields. Hands-on Experience - 2 4 years of experience in building and deploying statistical & ML/AI models. Preferred exposure includes use cases such as MMM, churn prediction, demand/sales forecasting, and transformer-based architectures. Programming Expertise - Strong command of Python for data science and machine learning workflows, including experience with packages like NumPy, Pandas, Scikit-learn, Optuna and deep learning frameworks such as PyTorch. Modular & Standards-Compliant Coding - Proficient in writing clean, modular, and maintainable code following PEP8 standards and software engineering best practices (including version control, documentation, and reusable components). ML Engineering Fundamentals - Proficient in both traditional machine learning algorithms and neural network architectures (e.g., feedforward, convolutional, transformer-based), with a solid grasp of data preprocessing, feature engineering, and model evaluation techniques. End-to-End Pipeline Exposure - Experience working across the entire model lifecycle from data ingestion and EDA to training, tuning, deployment, and monitoring. Business Communication - Ability to translate complex analytical results into clear, actionable insights for both technical and non-technical stakeholders. Core Competencies Required Effective Communication - Articulation of technical concepts, results, and recommendations across diverse teams. Mathematical Thinking & Creativity - Ability to approach problems with analytical rigor and devise innovative algorithmic solutions grounded in mathematical principles. Passion for New Ideas & Research - Curiosity and enthusiasm for exploring emerging techniques, experimenting with novel approaches, and contributing to innovation in AI/ML. Execution-Oriented Planning - Ability to structure complex problems and drive solutions with a balance of speed and accuracy. Cross-Functional Collaboration - Partnering with product, engineering, marketing, sales and leadership teams to align AI solutions with business goals.