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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist (Automotive Logistics, AWS Cloud) - **Company:** InnoCore Solutions, Inc. - **Location:** Dallas, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon S3, Artificial Neural Networks, Continuous Integration, IBM ILOG CPLEX Optimization Studio (CPLEX), Database Queries, Decision Support Systems, Distributed Computing Environment, Github, Integer Programming, Python (Programming Language), Linear Programming, Logistic Regression, Machine Learning, Delivery Pipeline, Snowflake, Apache Spark, Generative AI, AWS Lambda, Data Lakes, Information Technology, Machine Learning Operations, Cloudwatch, Dynamic Programming, Software Version Control, Network Optimization - **Published:** August 6, 2026 - **Apply:** https://www.dice.com/job-detail/621251eb-b048-47df-a715-6e6b4d3b6ccc ## About the Role * 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. ## Description * 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. ## Related Videos - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [WeAreDevelopers LIVE - CSS is DOOMed](https://www.wearedevelopers.com/videos/1838-wearedevelopers-live-css-is-doomed) - [Remote Driving on Plant Grounds with State-of-the-Art Cloud Technologies](https://www.wearedevelopers.com/videos/251-remote-driving-on-plant-grounds-with-state-of-the-art-cloud-technologies) - [Bringing AI Model Testing and Prompt Management to Your Codebase with GitHub Models](https://www.wearedevelopers.com/videos/1536-bringing-ai-model-testing-and-prompt-management-to-your-codebase-with-github-models) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) ## Related Articles - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [How software is steering vehicle technology](https://www.wearedevelopers.com/magazine/515-how-software-is-steering-vehicle-technology) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)