Senior Decision Scientist: Production AI & Optimization

Aimpoint Digital; United States; Remote; USD 150,000 - 210,000; Full time

Job summary

Aimpoint Digital is seeking a Lead Decision Scientist to join our decision sciences practice serving Federal clients. You will design, build, and deploy end-to-end analytical solutions using optimization, ML, and analytics, while mentoring junior scientists and contributing to practice development.

You will work on client engagements independently, manage tasks, write production-grade Python code, and take models from development into production following DecisionOps/MLOps practices.

Qualifications

• Active security clearance (Secret or higher)
• Willingness to work on-site with SIPR access 2–4 days/week
• MS/PhD in STEM fields or equivalent
• MS with 5–6 years practical experience or PhD with 4–5 years practical experience
• Proficiency in Python for data manipulation and modeling
• Experience with end-to-end model development and deployment

Responsibilities

• Design and build end-to-end analytical solutions from architecture to deployment
• Solve complex decision science use-cases with optimization, simulation, ML, and statistics
• Lead projects and mentor junior decision scientists
• Take models from development to production in client environments following best practices
• Collaborate with stakeholders and contribute to business development and GTM material

Skills

Python proficiency

Optimization knowledge

End-to-end modeling

Strong communication

Self-starter

Client engagement

Education

MS/PhD in STEM

MS with 5–6 years experience

PhD with 4–5 years experience

Tools

AIMMS/AMPL/GAMS/Pyomo

Gurobi/CPLEX/XPRESS

Job description

Aimpoint Digital is seeking a Lead Decision Scientist to join our decision sciences practice serving Federal clients. You will design, build, and deploy end-to-end analytical solutions using optimization, ML, and analytics, while mentoring junior scientists and contributing to practice development.

You will work on client engagements independently, manage tasks, write production-grade Python code, and take models from development into production following DecisionOps/MLOps practices.