Lead Data Scientist

Job Description

<p><b>Job Title: Data Science Lead</b></p><p><br></p><p><b>Role Overview</b></p><p>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.</p><p>The ideal candidate has extensive experience in machine learning, optimization, predictive modeling, and data engineering, combined with cross-functional leadership and stakeholder management.</p><p><br></p><p><b>Key Responsibilities:</b></p><p>1.<b> Leadership & Multi-Team Management</b></p><ul><li>Lead, mentor, and manage multiple teams of data scientists, ML engineers, and data engineers.</li><li>Allocate resources, define priorities, and oversee delivery across concurrent projects.</li><li>Establish technical standards, best practices, and reproducible workflows for all teams.</li><li>Foster a collaborative, innovative, and learning-oriented culture.</li><li>Align analytics strategy with business objectives through close collaboration with senior leadership and stakeholders.</li></ul><p>2<b>. Data Management & Infrastructure</b></p><ul><li>Oversee large-scale data ingestion, cleaning, transformation, and storage.</li><li>Ensure robust pipelines using PySpark, Pandas, NumPy, and SQL for distributed and vectorized processing.</li><li>Manage cloud-based environments and services (Azure Databricks, Blob Storage, Synapse Analytics).</li></ul><p>3. <b>Optimization & Operations Research</b></p><ul><li>Guide teams in designing optimization models for scheduling, routing, demand forecasting, and resource allocation.</li><li>Oversee solver selection and implementation, including Gurobi.</li><li>Advise on geospatial routing and analysis using OSMR.</li></ul><p>4. <b>Reporting, Visualization & Communication</b></p><ul><li>Lead creation of dashboards, KPIs, and visualizations using Plotly, Matplotlib, and Excel/SharePoint integration.</li><li>Communicate analytical insights and model results effectively to stakeholders at all levels.</li><li>Track operational and model performance via Azure Application Insights and other monitoring tools.</li></ul><p>5. <b>Technology & Execution Oversight</b></p><ul><li>Ensure teams effectively use development tools and environments, including Python 3.x, Poetry, Bash, Jupyter/Databricks Notebooks, and Git.</li><li>Guide deployment and orchestration of pipelines via CI/CD, Databricks clusters, and cloud-based solutions.</li><li>Promote scalable, reproducible, and maintainable architectures across all data science and analytics initiatives.</li></ul><p><br></p><p><b>Experience / Expertise:</b></p><ul><li>11+ years in data science, analytics, or operations research, including experience managing multiple technical teams.</li><li>Proven ability to deliver scalable, production-ready solutions in predictive modeling, optimization, or advanced analytics.</li><li>Hands-on experience with cloud-based analytics platforms (Azure Databricks, Blob, Synapse) and data engineering pipelines.</li><li>Strong background in machine learning, statistical modeling, operations research, and optimization algorithms.</li><li>Advanced algorithms and techniques applied in experience include:</li><li><b>Mixed-Integer Linear Programming (MILP), Constraint Programming (CP)</b></li><li><b>Traveling Salesman Problem (TSP) / Vehicle Routing Problem (VRP)</b></li><li>Regression and Classification models, Gradient Boosting (LightGBM), Logistic Regression</li><li>SHAP and feature importance for explainability.</li><li>Excellent communication skills for translating complex models and algorithms into actionable business insights</li></ul><p></p>