Data Scientist

Machine Learning-Lead Scientist / 5-7yrs Experience Location: Dalla/Fort Worth, TX Rate: $55 to $57 on W2 OR $60 to $63 on C2C Job Description: Top requirements: • Graduate degree in a quantitative field (Operations Research, Industrial Engineering, Applied Math, Data Science, etc.) • Strong Python development experience in building production applications • Proven experience designing and implementing deterministic optimization models using solvers such as Gurobi, CPLEX, or Xpress (ideally MIP-based Nice to have skills: • Experience with dynamic or stochastic optimization / programming • Background in machine learning / predictive modeling and integrating forecasts into optimization workflows • Familiarity with CI/CD pipelines, automated testing (unit, integration), and cloud-based deployments • Experience in database integration using SQL, MongoDB, and/or other • Experience with transportation, supply chain, service part logistics, or spare parts systems -Collaborate with leaders, business analysts, project managers, IT architects, technical leads and other engineers, along with internal customers, to understand requirements and develop needs according to business requirements for AI solutions -Maintain and enhance existing enterprise services, applications, and platforms using domain driven design and test-driven development -Troubleshoot and debug complex issues; identify and implement solutions -Create detailed project specifications, requirements, and estimates -Research and implement new AI technologies to enhance current processes, security, and performance -Work closely with data scientists and product teams to build and deploy machine learning models, focusing on the technical aspects of model deployment. -Implement and optimize Python-based ML pipelines for data preprocessing, model training, and deployment. -Monitor model performance and implement strategies for bias mitigation and explainability. Responsible for ensuring models are scalable and efficient in production environments. -Write and maintain code for model training and deployment, collaborating with software engineers to integrate models into applications. - Partner with a diverse team of experts, leveraging cutting-edge technologies to build scalable and impactful AI solutions. • Partner with OR&AA team members and IT to gather requirements, challenge assumptions, and influence product direction to deliver high-impact optimization capabilities. • Design, build, and enhance a Materials Distribution Optimizer (Python-based, deployed on KPaaS) leveraging Mixed Integer Programming (MIP). • Maintain and improve a production optimization system that runs on a recurring schedule (every ~20 minutes), ensuring reliability, scalability, and performance. • Develop and integrate data-driven enhancements into the optimizer, including predictive analytics to inform forward-looking decisions (e.g., demand, supply, disruptions). • Analyze historical data and build models (statistical or ML-based) to move from a greedy, point-in-time optimization approach toward more anticipatory decision-making. • Contribute to production-grade engineering practices: CI/CD pipelines, automated testing (unit, integration), and monitoring of model performance and application health. Describe a great candidate that you are looking for and what skills and experience they will have: A strong candidate is someone who can operate at the intersection of optimization, data science, and software engineering. They have hands-on experience building real-world optimization models (especially MIPs), and understand not just the math, but how to productionize and maintain these systems in a live environment. They are comfortable working in Python, writing clean, testable, and scalable code, and have experience deploying applications in cloud environments with CI/CD best practices. They bring curiosity and ownership—asking the right questions, understanding the business context, and proposing improvements beyond the immediate ask. Ideally, they also think beyond purely deterministic optimization, with an interest in incorporating predictive insights (ML/statistics) to improve decision quality over time. Experience in supply chain or transportation is a strong plus. Most importantly, they collaborate well, are pragmatic in their approach, and are motivated to build solutions that deliver measurable business impact. What is the team environment and structure like?: Will be working closely with other developers and ORAA team members in meeting the expectations/goals of the business. Expected to be vocal and present in various meetings within the team, and with our stakeholders.