Job description

We're looking for a Data Scientist to lead the design and development of optimization and machine learning models that allocate scarce parts and resources across competing programs. You'll build algorithms spanning mixed-integer programming, constraint optimization, and ML-driven forecasting that turn hard supply constraints into defensible, auditable allocation decisions. You'll own the full modeling lifecycle, from problem formulation and data exploration through validation and production deployment, partnering closely with program stakeholders to translate competing priorities into solvable objectives. This is a high-visibility role for someone who moves fluidly between rigorous quantitative modeling and pragmatic, program-facing communication. Key Responsibilities: • Design and implement optimization models (e.g., mixed-integer linear programming, constraint programming) for allocating scarce parts and resources across competing programs. • Develop ML models to forecast demand, supply risk, and part availability, feeding those forecasts directly into allocation logic. • Translate ambiguous, competing program priorities into formal objective functions and constraints. • Validate model outputs against historical allocation decisions and stakeholder expectations; iterate on formulations as new constraints emerge. • Build and maintain the data pipelines needed to keep optimization models current with live inventory, demand, and program data. • Present modeling tradeoffs and recommendations to non-technical program and supply-chain stakeholders. • Mentor junior data scientists on optimization techniques and modeling best practices. • Partner with software engineers to productionize models as scalable services. • Master's or PhD in Operations Research, Applied Mathematics, Computer Science, Industrial Engineering, or a related quantitative field (or equivalent practical experience). • 6+ years of experience building optimization and/or ML models for resource allocation, scheduling, or supply chain problems. • Deep hands-on experience with optimization solvers (e.g., Gurobi, CPLEX, OR-Tools) and formulating MILP/constraint optimization problems. • Strong Python skills, with experience in ML frameworks (e.g., scikit-learn, PyTorch) and data manipulation libraries (pandas, NumPy). • Experience working with messy, real-world supply chain, inventory, or program data. • Excellent communication skills; comfortable presenting complex tradeoffs to program and business stakeholders. Preferred Qualifications: • Experience with allocation problems in aerospace, defense, or manufacturing supply chains. • Familiarity with ERP systems (e.g., SAP) and how allocation decisions flow into procurement and production planning. • Experience deploying optimization models as production services (APIs, batch pipelines). • Exposure to reinforcement learning or Bayesian methods for decision-making under uncertainty. We Value: • Drive: Passion and energy to implement quality technical solutions. Self-motivation and intellectual curiosity • Commitment to Quality: Passion to conceive and produce world-class solutions that drive real-world value for the customer • Customer Focus: Consultative approach to solving problems for customers. Expectations management. • Communication: Superior communication skills. Ability to clearly articulate problems, solutions, risks, rewards etc. (written and verbal) • Technical Skills: Love for technology. You have to be inherently passionate about technology. • Business Acumen: Technology ultimately is used to enable the business. We look for people who understand how the businesses can be enabled through their technical solutions All your information will be kept confidential according to EEO guidelines.

Requirements

Must-haves

Design and implement optimization models (e.g., mixed-integer linear programming, constraint programming) for allocating scarce parts and resources across competing programs.
Develop ML models to forecast demand, supply risk, and part availability, feeding those forecasts directly into allocation logic.
Translate ambiguous, competing program priorities into formal objective functions and constraints.
Validate model outputs against historical allocation decisions and stakeholder expectations; iterate on formulations as new constraints emerge.
Build and maintain the data pipelines needed to keep optimization models current with live inventory, demand, and program data.
Present modeling tradeoffs and recommendations to non-technical program and supply-chain stakeholders.
Mentor junior data scientists on optimization techniques and modeling best practices.
Partner with software engineers to productionize models as scalable services.
Master's or PhD in Operations Research, Applied Mathematics, Computer Science, Industrial Engineering, or a related quantitative field (or equivalent practical experience).
6+ years of experience building optimization and/or ML models for resource allocation, scheduling, or supply chain problems.
Deep hands-on experience with optimization solvers (e.g., Gurobi, CPLEX, OR-Tools) and formulating MILP/constraint optimization problems.
Strong Python skills, with experience in ML frameworks (e.g., scikit-learn, PyTorch) and data manipulation libraries (pandas, NumPy).
Experience working with messy, real-world supply chain, inventory, or program data.
Excellent communication skills; comfortable presenting complex tradeoffs to program and business stakeholders.
Drive: Passion and energy to implement quality technical solutions. Self-motivation and intellectual curiosity
Commitment to Quality: Passion to conceive and produce world-class solutions that drive real-world value for the customer
Customer Focus: Consultative approach to solving problems for customers. Expectations management.
Communication: Superior communication skills. Ability to clearly articulate problems, solutions, risks, rewards etc. (written and verbal)
Technical Skills: Love for technology. You have to be inherently passionate about technology.
Business Acumen: Technology ultimately is used to enable the business. We look for people who understand how the businesses can be enabled through their technical solutions

Nice-to-haves

Experience with allocation problems in aerospace, defense, or manufacturing supply chains.
Familiarity with ERP systems (e.g., SAP) and how allocation decisions flow into procurement and production planning.
Experience deploying optimization models as production services (APIs, batch pipelines).
Exposure to reinforcement learning or Bayesian methods for decision-making under uncertainty.

Application prep

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Keywords to mirror

machine learningmlpython

Platform: smartrecruiters
Effort: medium