Data Scientist: Optimization & ML for Resource Allocation

Talentify; United States; Remote; USD 140,000 - 210,000; Full time

Job summary

Oteemo is seeking a data scientist to lead optimization and ML model development for allocating scarce parts across programs. You will own the modeling lifecycle from problem framing to production deployment, partnering with stakeholders to translate priorities into solvable objectives.

The role requires deep optimization experience, strong Python skills, and ability to communicate complex tradeoffs to non-technical audiences.

Qualifications

• Master's or PhD in quantitative field or equivalent experience.
• 6+ years building optimization or ML models for allocation problems.
• Hands-on experience with solvers (Gurobi, CPLEX, OR-Tools) and MILP/constraint optimization.
• Strong Python skills; experience with ML frameworks and data manipulation libraries (pandas, NumPy).
• Experience with real-world supply chain, inventory, or program data.
• Excellent communication skills for stakeholder presentations.

Responsibilities

• Design and implement optimization models for allocating scarce parts and resources.
• Develop ML models to forecast demand, supply risk, and part availability.
• Translate competing program priorities into formal objective functions and constraints.
• Validate model outputs against historical data and stakeholder expectations.
• Build data pipelines to keep models current with live inventory and demand data.
• Present tradeoffs and recommendations to program and business stakeholders.
• Mentor junior data scientists on optimization techniques.
• Collaborate with engineers to productionize models as scalable services.

Skills

Optimization modeling

Python

ML frameworks

Data manipulation

Education

Master's or PhD in a quantitative field

Tools

Gurobi

CPLEX

OR-Tools

Python

scikit-learn

PyTorch

Pandas

NumPy

Job description

Oteemo is seeking a data scientist to lead optimization and ML model development for allocating scarce parts across programs. You will own the modeling lifecycle from problem framing to production deployment, partnering with stakeholders to translate priorities into solvable objectives.

The role requires deep optimization experience, strong Python skills, and ability to communicate complex tradeoffs to non-technical audiences.