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:

• Masters 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.