Data Scientist

Key Responsibilities:Develop, validate, and deploy predictive, prescriptive, and scoring models to power product features and business decisions.Partner with the product management and data engineering teams to design and implement algorithms that directly impact customer experience and business growth.Drive feature engineering, model selection, and performance evaluation across diverse modeling use cases (scoring, forecasting, optimization, segmentation, simulation, NLP, etc.).Make analytical and technical decisions on modeling trade-offs (accuracy, interpretability, scalability).Ensure models are production-grade, explainable, monitored, and continuously improved as data and market conditions evolve.Stay up to date with emerging ML/AI techniques and proactively evaluate their applicability to business use cases.Required Skills:Strong foundation in Machine Learning, Statistical Modeling, and Applied Mathematics, with proven experience in real-world problem-solving.Hands-on expertise in Lightweight MMM, Meridian, PyMC, Bayesian modelsHands-on expertise in Python and R, including ML libraries (scikit-learn, XGBoost, PyTorch/TensorFlow for deep learning)Solid understanding of data preprocessing, feature engineering, and handling large-scale structured and unstructured datasets.Experience in building and deploying models such as: Scoring/response models, recommendation systems, forecasting, optimization, segmentation, causal inference.Excellent communication and stakeholder management skills, with the ability to effectively influence, align, and drive consensus across product, engineering, and business teams.Proven track record of leading analytics/modeling projects end-to-end.Desired Skills:Exposure to Text Mining and NLP (topic modeling, sentiment analysis, embeddings)Knowledge of LLM-based applications is a plus.Knowledge of Bayesian analysis and probabilistic modeling.Experience with optimization algorithms, reinforcement learning, or simulation modeling.Working knowledge of cloud platforms (AWS) and ML pipelines is a plus.Exposure to Deep LearningFamiliarity with digital marketing, SEO, and 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).6–10 years of experience in applied data science/modeling, ideally with projects spanning MMM, MTA, predictive modeling, NLP, optimization, and business-focused analytics.Experience delivering models into production environments (not just research/prototyping). ******Must be available immediately *****

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