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 machine learning, MLOps best practices, and AWS-native services.

Responsibilities:

• Design, train, and deploy advanced time series and regression-based forecasting models (e.g., ARIMA, Prophet, XGBoost, LSTM) for vehicle demand, part shipments, and inventory needs.
• Incorporate structured and unstructured data from ERP, telematics, dealer orders, production plans, and real-time logistics feeds.
• 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.
• Lead the design of scalable ETL/ELT pipelines using AWS Glue, Apache Spark, and AWS Step Functions.
• Manage data ingestion from diverse sources (S3, Redshift, Snowflake, RDS, Kinesis) with robust governance and lineage tracking.
• 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:

• At least 2 years of experience in applying statistical and machine learning techniques to real-world problems.
• Strong experience in Python, with ML libraries such as scikit-learn, TensorFlow, PyTorch, and XGBoost.
• 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, Industrial Engineering, or Applied Mathematics.