Data Scientist (Automotive Logistics, AWS Cloud)

We are hiring a Lead Data Scientist to drive the architecture, development, and deployment of machine learning and AI-powered demand forecasting solutions within the automotive logistics ecosystem. This role is central to improving vehicle and parts supply chain visibility, inventory accuracy, and fulfillment predictability using Linear Programming (LP), Dynamic Programming (DP), machine learning, MLOps best practices, and AWS-native services.

Responsibilities:

• Develop and implement optimization models using Linear Programming (LP), Mixed Integer Linear Programming (MILP), and Dynamic Programming (DP) to solve complex supply chain problems, including inventory optimization, production planning, transportation, and network optimization.
• Build scalable optimization solutions using Python and optimization libraries such as Pyomo, PuLP, OR-Tools, Gurobi, or CPLEX, and deploy them on cloud platforms.
• Collaborate with supply chain, operations, and business stakeholders to translate complex business requirements into mathematical optimization models and decision-support solutions.
• Continuously monitor model accuracy and improve forecasts based on error analysis, drift detection, and business input.
• Align modeling strategy with supply chain KPIs such as fill rate, inventory turnover, order-to-ship lead time, and forecast bias.
• Develop and manage end-to-end ML pipelines using Amazon SageMaker Pipelines, AWS Step Functions, and CodePipeline.
• Automate model training, testing, deployment, monitoring, and rollback using CI/CD practices tailored for ML.
• Implement SageMaker Model Monitor, SageMaker Clarify, and CloudWatch for continuous model performance, bias, and drift monitoring.
• Use AWS Lambda and EventBridge to integrate real-time triggers for retraining or alerts.
• Define and operate a SageMaker Feature Store for training and inference consistency.
• Develop AI-driven decision support tools using classification models, clustering, anomaly detection, and explainable AI (XAI).
• Explore use of Generative AI for scenario simulation, forecast explanation, and automated reporting.
• Collaborate with business teams to embed AI recommendations into dashboards, alerts, or APIs.
• Act as a bridge between technical teams and business stakeholders (supply chain, logistics, planning).
• Promote best practices in model documentation, reproducibility, testing, and governance.

Requirements:

• Experience developing optimization models using Linear Programming (LP), Mixed Integer Linear Programming (MILP), Dynamic Programming (DP), or other Operations Research techniques.
• Proficiency in Python and experience with optimization frameworks such as Pyomo, PuLP, Google OR-Tools, Gurobi, or IBM CPLEX.
• At least 2 years of experience in applying statistical and machine learning techniques to real-world problems.
• Solid understanding of forecasting techniques, statistical modeling, and time series analysis.
• Knowledge of methods like Logistic Regression, Time Series Analysis, GLMs, Mixed Modeling, Multivariate Statistics, Predictive Modeling, Decision Trees, Gradient-Boosted Trees, Random Forests, and Neural Networks.
• Hands-on experience with AWS services: Amazon SageMaker, S3, Glue, Lambda, CloudWatch, Step Functions, ECR, CodePipeline.
• Strong SQL skills and familiarity with data lakes, Redshift/Snowflake, and distributed data processing (Spark).
• Experience implementing MLOps pipelines in production environments.
• Deep understanding of automotive logistics, including order lifecycle, dealer distribution, parts inventory, and transportation flows.
• Experience with version control systems such as GitHub, and familiarity with CI/CD practices to streamline model deployment and code management.
• Prior experience with demand forecasting or supply chain analytics at scale.

Education:

• Advanced degree (MS or PhD) in a quantitative field including but not limited to Statistics, Computer Science/Data Science, Operations Research, Industrial Engineering, or Applied Mathematics.