Senior Data Science Manager

Role & responsibilities

• Lead and manage a team of Data Scientists, ML Engineers, and Data Analysts to deliver high-impact data science solutions.
• Define and execute the data science roadmap and strategy aligned with business and product objectives.
• Translate complex business problems into data-driven solutions, predictive models, and actionable insights.
• Lead development of machine learning, statistical, predictive, NLP, recommendation, forecasting, and GenAI models.
• Establish best practices for data exploration, feature engineering, model development, experimentation, validation, and deployment.
• Drive A/B testing, experimentation, causal analysis, and statistical modeling to support product and business decisions.
• Partner with Product, Engineering, Business, Marketing, Finance, and Leadership teams to identify and prioritize opportunities.
• Define and track KPIs, model performance metrics, business impact, and ROI of data science initiatives.
• Oversee the transition of models from research/PoC to reliable production systems in collaboration with ML and Engineering teams.
• Ensure appropriate practices for data quality, model governance, privacy, security, fairness, and responsible AI.
• Review analytical methodologies, statistical approaches, models, and technical deliverables.
• Mentor Data Scientists and develop a strong culture of technical excellence, experimentation, documentation, and knowledge sharing.
• Manage project timelines, resources, priorities, and dependencies across multiple data science initiatives.
• Evaluate emerging AI/ML, GenAI, and analytics technologies and identify opportunities for business adoption.
• Present complex analytical findings and recommendations clearly to senior leadership and non-technical stakeholders.

Preferred candidate profile

• 5 to 10 years of experience in Data Science, Machine Learning, Advanced Analytics, or a related field, including experience leading teams or major data science initiatives.
• Strong expertise in Python, SQL, statistics, probability, machine learning, and predictive modeling.
• Strong understanding of supervised/unsupervised learning, feature engineering, model evaluation, optimization, and statistical inference.
• Experience with PyTorch, TensorFlow, Scikit-learn, XGBoost, Pandas, NumPy, or equivalent tools.
• Experience with A/B testing, experimentation, hypothesis testing, causal inference, and product/business analytics.
• Knowledge of Generative AI, LLMs, NLP, recommendation systems, time-series forecasting, or other advanced ML techniques is highly desirable.
• Experience with cloud platforms such as AWS, Azure, or GCP and modern data/ML platforms such as Databricks or Snowflake.
• Working knowledge of MLOps, MLflow, model deployment, model monitoring, and production ML workflows.
• Strong understanding of data engineering, ETL/ELT, data warehouses, data lakes, and distributed data processing.
• Proficiency with visualization and BI tools such as Tableau, Power BI, Looker, or equivalent.
• Demonstrated ability to lead cross-functional projects and mentor/manage Data Science teams.
• Strong business acumen with the ability to connect analytical work to revenue, cost reduction, customer experience, risk, or other measurable business outcomes.
• Excellent communication, stakeholder management, strategic thinking, and presentation skills.
• Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, Economics, or a related quantitative field; Master's degree preferred.
• Experience with responsible AI, data governance, model governance, and data privacy is an advantage.