we are seeking an experienced Data Scientist / Optimization Developer to support the design, development, and deployment of optimization solutions for complex automotive supply chain challenges.
Location: Dallas, TX
Work Model: 4 days onsite per week
Project Status: Funded and actively staffed
Schedule: 45 hours/week (9 hours/day, no overtime)
What You'll Do
- Develop and implement mathematical optimization models using Gurobi to solve large-scale supply chain problems.
- Design decision-support solutions for demand planning, inventory optimization, production scheduling, transportation, logistics, and network flow optimization.
- Build and support cloud-based optimization applications leveraging AWS technologies.
- Analyze large operational datasets to identify opportunities for efficiency improvements across the supply chain.
- Integrate optimization engines with enterprise data platforms, APIs, and business applications.
- Create analytical tools, dashboards, and visualizations to communicate recommendations to business stakeholders.
- Collaborate closely with supply chain planners, operations teams, product owners, and data engineers.
Required Qualifications
- Bachelor's or Master's degree in Operations Research, Applied Mathematics, Industrial Engineering, Computer Science, or a related field.
- 3-5+ years of experience in optimization, data science, or advanced analytics.
- Strong hands-on experience with Gurobi and mathematical optimization techniques, including linear programming, mixed-integer programming, and heuristic methods.
- Proven experience developing solutions on AWS, including services such as EC2, S3, Lambda, Glue, ECS, EKS, RDS, DynamoDB, Step Functions, or SageMaker.
- Strong programming and problem-solving skills with the ability to work on complex operational challenges.
- Experience translating business requirements into scalable analytical and optimization solutions.
Preferred Background
Experience in the automotive industry, supply chain analytics, manufacturing operations, logistics, or production planning is highly desirable.