Senior Data Scientist, Supply Chain Forecasting and Optimization

Job Description

A leading healthcare-focused organization is seeking a Senior Data Scientist to support forecasting, inventory optimization, and data-driven decision-making across supply chain operations. This position requires a hands-on analytics professional who can develop predictive models, translate business challenges into practical solutions, and deliver actionable insights that improve operational performance.

The successful candidate will partner closely with business stakeholders, balancing advanced data science techniques with pragmatic problem-solving to achieve measurable outcomes. This role requires the ability to move seamlessly between long-term analytical initiatives and rapidly evolving business priorities.

Responsibilities

• Design, develop, and refine forecasting models that support supply chain planning and operational decisions.
• Build optimization models that improve inventory management, product allocation, and overall operational efficiency.
• Conduct exploratory data analysis to uncover trends, risks, opportunities, and business insights.
• Translate stakeholder questions into analytical approaches and data-driven recommendations.
• Collaborate with business partners to gather requirements, communicate findings, and support implementation of insights.
• Develop reusable analytical frameworks, reporting solutions, and decision-support assets.
• Work with large and complex datasets to create scalable analytical workflows and predictive solutions.
• Deliver insights through clear data visualization, reporting, and business-focused storytelling.
• Support both strategic analytics initiatives and time-sensitive business requests.
• Recommend practical analytical approaches that maximize business value and operational impact.

Required Experience and Skills

• Strong experience in predictive modeling, forecasting, statistical analysis, and applied data science.
• Demonstrated success delivering analytical solutions that drive measurable business results.
• Advanced proficiency in Python for model development, data analysis, and automation.
• Strong SQL skills with experience querying and transforming large-scale datasets.
• Experience working in cloud-based analytics environments, including Databricks, Azure, or GCP.
• Ability to build scalable, production-quality analytical workflows and data science solutions.
• Strong problem-solving skills with the ability to select appropriate analytical methods based on business needs.
• Experience communicating complex analytical concepts to both technical and non-technical audiences.
• Proven ability to manage competing priorities across strategic initiatives and ad hoc analytical requests.
• Strong stakeholder engagement, collaboration, and business partnership skills.
• Ability to present findings clearly and influence decision-making through data-driven recommendations.

Preferred Experience

• Supply chain analytics and operational performance optimization.
• Demand forecasting and inventory planning.
• Inventory optimization and allocation modeling.
• Operations research methodologies and optimization techniques.
• Healthcare, pharmacy, retail, or related industry analytics experience.
• Experience with business intelligence and visualization platforms such as Tableau, Looker, ThoughtSpot, or Power BI.

FAQ

1. What does a Senior Data Scientist specializing in supply chain forecasting and optimization do?

A Senior Data Scientist develops forecasting and optimization models that support inventory, demand, fulfillment, procurement, and logistics decisions. The role combines statistical modeling, machine learning, optimization techniques, and business analysis to improve planning accuracy and operational efficiency.

2. What types of supply chain problems are addressed in this role?

Typical problems include demand forecasting, inventory optimization, replenishment planning, transportation analysis, capacity planning, supplier performance, and service-level management. The goal is to identify patterns and recommend decisions that balance availability, cost, and operational constraints.

3. How are demand forecasting models developed?

Forecasting models use historical demand, seasonality, trends, promotions, pricing, lead times, and other relevant variables to estimate future demand. Data Scientists compare different statistical and machine learning approaches and select models based on forecast accuracy and business requirements.

4. What optimization techniques are commonly used?

Depending on the problem, the role may use linear programming, mixed-integer optimization, constraint-based methods, simulation, or heuristic approaches. These techniques help identify decisions that maximize desired outcomes while accounting for capacity, inventory, cost, service, and operational constraints.

5. Which technologies are commonly used for supply chain analytics?

Python and SQL are commonly used for data preparation, modeling, forecasting, and analysis. Data Scientists may also work with cloud data platforms, machine learning libraries, visualization tools, optimization solvers, and workflow platforms for production analytics.

6. How are forecast accuracy and model performance measured?

Forecast performance can be evaluated using measures such as MAE, RMSE, MAPE, weighted error metrics, or business-specific service-level measures. Optimization models may also be assessed through cost savings, inventory reductions, service-level improvements, utilization, or other operational outcomes.