Job Type: Full-time
Experience Level: Lead / Senior Management (5+ years of relevant experience)
About the Role
We are seeking a Lead Data Scientist with strong expertise in core, traditional data science methodologies. In this role, you will lead our analytics initiatives by applying classical machine learning algorithms, advanced statistical modeling, hypothesis testing, and predictive analytics to solve critical business problems.
You will take technical ownership of the entire data science pipeline—from feature engineering and exploratory data analysis to model building, validation, and production deployment. You will also mentor mid-level data scientists and bridge the gap between technical execution and key business stakeholders.
Key Responsibilities
Model Development: Design, build, and evaluate robust classical machine learning models (e.g., Logistic/Linear Regression, XGBoost, Random Forests, Time-Series Forecasting, Clustering) to tackle complex business problems.
Statistical Analysis & Research: Conduct rigorous statistical testing, hypothesis testing, exploratory data analysis (EDA), and experimental design (A/B testing).
End-to-End Pipeline Ownership: Own the complete data science lifecycle, including data extraction, cleaning, advanced feature engineering, model tuning, validation, and deployment into production workflows.
Technical Leadership & Mentorship: Guide, review, and mentor junior and mid-level data scientists, ensuring high standards in code quality and statistical rigor.
Stakeholder Collaboration: Translate ambiguous business requirements into technical data science roadmaps and present actionable, data-driven insights to executive stakeholders.
Requirements
Experience: 5+ years of professional data science experience, with a proven track record of delivering traditional ML models to production environments.
Core Technical Stack: Advanced proficiency in Python or R, and expert-level mastery of SQL for complex querying and data preparation.
Machine Learning & Statistics: Deep theoretical and practical knowledge of supervised/unsupervised learning, probability, regression analysis, classification, and time-series models.
Tooling: Proficiency with core analytical libraries (e.g., scikit-learn, pandas, numpy, statsmodels, LightGBM/XGBoost).
Deployment & MLOps: Experience deploying predictive models into REST APIs or batch prediction pipelines using containerization (Docker) and ML pipelines.
Education: Bachelor's or Master's degree in a quantitative field such as Statistics, Mathematics, Computer Science, Economics, or a related quantitative discipline.
Nice to Have
Master’s or Ph.D. in Statistics, Applied Mathematics, or a quantitative field.
Experience with cloud analytics platforms (AWS SageMaker, GCP Vertex AI, or Azure ML).
Experience working with big data tools (PySpark, Databricks, Snowflake).
