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 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.

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• 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 Learning
• Familiarity with digital marketing, SEO, and search-related modeling is a plus.

QUALIFICATIONS:

• Masters or PhD in a quantitative field (Computer Science, Statistics, Applied Mathematics, Data Science, Operations Research, Economics, Engineering).
• 46 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 (not just research/prototyping).