Lead Data Scientist

Job Title: Data Science Lead

Role Overview

The Data Science Lead is responsible for managing multiple data science and analytics teams, driving strategic initiatives, and delivering high-impact data-driven solutions. This role blends technical expertise, leadership, and strategic oversight, ensuring analytics efforts are aligned with business objectives, scalable, and reproducible.

The ideal candidate has extensive experience in machine learning, optimization, predictive modeling, and data engineering, combined with cross-functional leadership and stakeholder management.

Key Responsibilities:

1. Leadership & Multi-Team Management

• Lead, mentor, and manage multiple teams of data scientists, ML engineers, and data engineers.
• Allocate resources, define priorities, and oversee delivery across concurrent projects.
• Establish technical standards, best practices, and reproducible workflows for all teams.
• Foster a collaborative, innovative, and learning-oriented culture.
• Align analytics strategy with business objectives through close collaboration with senior leadership and stakeholders.

2. Data Management & Infrastructure

• Oversee large-scale data ingestion, cleaning, transformation, and storage.
• Ensure robust pipelines using PySpark, Pandas, NumPy, and SQL for distributed and vectorized processing.
• Manage cloud-based environments and services (Azure Databricks, Blob Storage, Synapse Analytics).

3. Optimization & Operations Research

• Guide teams in designing optimization models for scheduling, routing, demand forecasting, and resource allocation.
• Oversee solver selection and implementation, including Gurobi.
• Advise on geospatial routing and analysis using OSMR.

4. Reporting, Visualization & Communication

• Lead creation of dashboards, KPIs, and visualizations using Plotly, Matplotlib, and Excel/SharePoint integration.
• Communicate analytical insights and model results effectively to stakeholders at all levels.
• Track operational and model performance via Azure Application Insights and other monitoring tools.

5. Technology & Execution Oversight

• Ensure teams effectively use development tools and environments, including Python 3.x, Poetry, Bash, Jupyter/Databricks Notebooks, and Git.
• Guide deployment and orchestration of pipelines via CI/CD, Databricks clusters, and cloud-based solutions.
• Promote scalable, reproducible, and maintainable architectures across all data science and analytics initiatives.

Experience / Expertise:

• 11+ years in data science, analytics, or operations research, including experience managing multiple technical teams.
• Proven ability to deliver scalable, production-ready solutions in predictive modeling, optimization, or advanced analytics.
• Hands-on experience with cloud-based analytics platforms (Azure Databricks, Blob, Synapse) and data engineering pipelines.
• Strong background in machine learning, statistical modeling, operations research, and optimization algorithms.
• Advanced algorithms and techniques applied in experience include:
• Mixed-Integer Linear Programming (MILP), Constraint Programming (CP)
• Traveling Salesman Problem (TSP) / Vehicle Routing Problem (VRP)
• Regression and Classification models, Gradient Boosting (LightGBM), Logistic Regression
• SHAP and feature importance for explainability.
• Excellent communication skills for translating complex models and algorithms into actionable business insights