Data Science Manager

Role description

Data Science Manager / Lead Data Scientist

technical excellence, strong problem-solving skills, and stakeholder leadership

Key Responsibilities

Technical Leadership & Delivery

• machine learning models




• Python (pandas, scikit-learn, statsmodels, PySpark optional)



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  Conduct exploratory data analysis (EDA), feature engineering, model development, validation, and deployment.



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  Oversee the end-to-end lifecycle of DS projectsfrom problem scoping to delivery.



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  Ensure model interpretability, robustness, reproducibility, and performance monitoring.

Advanced Analytics & Methodology

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  Develop forecasting solutions using methods such as:

  
  
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    ARIMA, SARIMA, ETS, Prophet, LSTM (optional)

  
  
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    Hierarchical/ensemble forecasting

  
  
  
  


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  Build causal inference and impact-measurement models using approaches like:

  
  
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    Difference-in-differences, synthetic control, uplift modeling, regression discontinuity, causal trees

  
  
  
  



• price elasticity



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  Design experiments (A/B tests) and statistical testing frameworks.

Data Engineering & Tools

• SQL



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  Build robust data pipelines and collaborate with data engineering teams for productionization.




• AWS/Azure/GCP

Stakeholder Management & Business Partnership

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  Understand business problems across product, marketing, operations, commercial, or supply chain domains.



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  Translate ambiguous problems into measurable data science solutions.



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  Present insights, models, and recommendations to leadership in a clear, actionable manner.



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  Manage expectations, timelines, and delivery quality for multiple stakeholders.

Team Leadership & Mentoring

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  Guide, mentor, and upskill junior and mid-level data scientists.



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  Conduct code reviews, enforce best practices, and maintain technical excellence across the team.



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  Foster a culture of innovation, testing, and continuous improvement.

Required Qualifications

• 810 years of experience




• Python (pandas, NumPy, scikit-learn, statsmodels)




• SQL




• classical ML algorithms




• forecasting, causal inference



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  Proven experience delivering end-to-end data science projects independently.



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  Ability to explain complex analyses and model results to non-technical stakeholders.

Preferred Skills

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  Experience with cloud platforms (AWS, Azure, GCP).



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  Familiarity with MLflow, Databricks, or data pipelines (Airflow, Prefect, DBT).



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  Exposure to A/B testing, experimental design, or uplift modeling.



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  Knowledge of visualization tools (Tableau, Power BI, Plotly).



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  Domain experience in Retail, CPG, Finance, E-commerce, or Supply Chain (optional but beneficial).