Staff Data Scientist

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:

Data preprocessing, feature engineering, model training

Validation, offline evaluation, and sensitivity analysis

Monitoring, 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

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.

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 vector embeddings and vector databases 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.

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.