Data scientist-- Dallas TX Need only local profile—F2F Mandate

Hi All,

Good Morning,

Immedaite and urgent need for thr below requirement.

Title: Data scientist-- Dallas TX Need only local profile—F2F Mandate

Location: Dallas TX

Duration: Long Term

Job description outlining day-to-day responsibilities (provide as much detail as possible and/or attach any necessary docs):

·         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.

Top 3 Mandatory Skills and Experience (EX: education, software skills, licenses or certifications, etc.):

·         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 modelsusing solvers such as Gurobi, CPLEX, or Xpress (ideally MIP-based)

 Nice to Have Skills and Experience (preferred but not required):

·         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

 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. Expected to be in the office Tuesdays, Wednesdays and Thursdays. Mondays and Fridays are flexible and can be remote.

 How will the resource(s) fit into your team?:

·         They will be treated as an equal part of the team, expected to provide insights and deliver on key features.