Sr Data Scientist

Key Responsibilities

• Modeling & Forecasting:

Develop and maintain predictive models across supply chain and inventory initiatives — including forecasting models, classification, regression, clustering, and segmentation tasks.

• Optimization & Simulation:

Build and refine models for network optimization, inventory allocation, sourcing, and internal transfers — using discrete optimization, simulation, and heuristic/metaheuristic techniques.

• Exploratory Analysis & Feature Development:

Use EDA and statistical analysis to develop features, understand drivers of performance, and improve model design.

• Time Series Analysis:

Design, test, and deploy time series models for demand forecasting, product performance tracking, and lifecycle modeling.

• Cross-Functional Collaboration:

Work closely with engineering, product, and operations partners to frame problems, communicate insights, and translate models into decisions and tools.

• Tooling & Automation:

Build scalable pipelines and decision-support tools using Python, Spark, and cloud-based infrastructure.

Qualifications

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Operations Research, or a related field
• 5+ years of experience building and deploying models in a production or operational environment
• Proficiency in Python (Pandas, Scikit-learn, Num Py) and SQL; experience with Spark or other distributed frameworks
• Demonstrated experience with supervised learning (regression, classification), unsupervised learning (clustering, dimensionality reduction), and model evaluation techniques
• Strong background in time series forecasting, including both classical (e.g., ARIMA, exponential smoothing) and ML-based methods (e.g., XGBoost, LSTM, DeepAR)
• Experience applying discrete optimization (e.g., MIP, constraint solvers, genetic algorithms) to real-world problems
• Familiarity with simulation-based modeling and tradeoff analysis
• Experience with data visualization tools (e.g., Superset, Tableau) and stakeholder-facing communication

Strong communication skills, with the ability to explain complex modeling approaches to technical and non-technical audiences