Senior Data Scientist

Key Responsibilities: Develop, validate , and deploy predictive, prescriptive, and scoring models to power product features and business decisions. Conduct deep-dive analyses to extract meaningful insights from complex and large datasets; identify key drivers, patterns, and opportunities. Partner with the product management and data engineering teams to design and implement algorithms that directly impact customer experience and business growth. Own end-to-end model lifecycle management, including: D ata preprocessing, feature engineering, model training V alidation, offline evaluation, and sensitivity analysis M onitoring, drift detection, and iterative improvements Make analytical and technical decisions on modeling trade-offs (accuracy, interpretability, scalability) and ensure outputs are aligned with business objectives . Ensure machine learning models are explainable, reproducible, and aligned with business objectives . Present findings and recommendations to key stakeholders in a clear and actionable manner. Drive experimentation through A/B testing and offline validation to evaluate model performance. Stay up to date with emerging ML/AI techniques and proactively evaluate their applicability to business use cases. Mentor and guide junior data scientists /analysts accelerating their technical growth and career development Required Skills: Strong foundation in Machine Learning, Statistical Modeling, and Applied Mathematics, with proven experience in real-world problem-solving. Strong software engineering skills with proficiency in Python and R, including ML libraries (scikit-learn, XGBoost , PyTorch /TensorFlow for deep learning) Solid experience with data preprocessing, feature engineering, and working with large structured and unstructured datasets. Experience in building and deploying models such as: Scoring/response models, recommendation systems, forecasting, optimization, segmentation , causal inference. Strong collaboration skills with the ability to work closely with product, engineering, and business stakeholders. Proven track record of owning analytics or modeling projects end-to-end. Desired Skills: Knowledge of Bayesian analysis and probabilistic modeling. Experience applying optimization or simulation techniques to real-world decision problems Exposure to Text Mining and NLP (topic modeling, sentiment analysis, embeddings) Experience working with large v ector e mbeddings and v ector d atabase s is a plus. Knowledge of LLM-based applications is a plus . Working knowledge of cloud platforms (AWS) and ML pipelines is a plus . Background in digital marketing analytics , including SEO, paid media or search-related modeling is a plus . Qualifications: Master’s or PhD in a quantitative field (Computer Science, Statistics, Applied Mathematics, Data Science, Operations Research, Economics, Engineering). 4– 6 years of experience in applied data science/modeling, ideally with projects spanning predictive modeling, NLP, optimization, and business-focused analytics. Experience delivering models into production environments.