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.

- 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 :

- 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 (not just research/prototyping).