Note: The job is a remote job and is open to candidates in USA. Aimpoint Digital is an AI and data consulting firm that delivers production solutions built on data and analytics foundations. The Principal Decision Scientist will design, develop, deploy, and maintain end-to-end analytical solutions for Federal clients using optimization, simulation, machine learning, statistical modeling, and analytics while leading projects, mentoring junior staff, and contributing to business development and practice initiatives.
Responsibilities
- Become a trusted advisor working with clients to design and build end-to-end analytical solutions from initial solution architecture design through feature engineering, modeling, deployment, and maintenance
- Work independently to solve complex decision science use-cases across various industries using mathematical optimization, simulation, machine learning, statistical/mathematical modeling, and analytics to solve use cases for our federal clients
- Use your experience with decision science to build and manage agentic workflows to complete modeling tasks
- Flexibly lead projects, from hands-on single developer engagements through complex projects coordinating between account management and a team of developers across Aimpoint
- Manage or mentor junior decision scientists through their career at Aimpoint
- Synthesize insights and construct narratives to influence decision making using optimization, simulation, statistics, ML modeling, and other techniques
- Write code in Python following software engineering best practices
- Take models from development to production in client environments following DecisionOps/MLOps best practices
- Collaborate with stakeholders and customers to ensure successful project delivery
- Assist with technical proposal and GTM material development
- Contribute to the Aimpoint perspective on agentic and AI accelerated decision science
- Lead and deliver internal practice development initiatives
Skills
- Active Secret or higher security clearance
- MS/PhD in Operations Research, Industrial Engineering, Computer Science, Mathematics, Engineering, or other STEM-related field
- MS + 5-6 years practical experience
- PHD + 4-5 years practical experience
- Strong theoretical knowledge of optimization techniques, including linear programming and integer programming and/or dynamic programming and graph theory
- Proficiency in a programming language such as Python, and/or proficiency in an optimization platform, AIMMS/AMPL/GAMS/Pyomo
- Practical experience with open-source solvers and commercial solvers, such as Gurobi, CPLEX or XPRESS
- Required competency in Python for data manipulation and modeling via classical ML methodologies
- Demonstrated evidence of experience with end-to-end model development including but not limited to:
- Requirements gathering
- Solution design / architecture
- EDA / data validation
- Model development and testing
- Model deployment
- Model maintenance
- Business user handoff / training
- Experience communicating complex topics and results to high-level stakeholders
- Strong written and verbal communication skills are required
- Self-starter with excellent communication skills, able to work independently, and lead projects, initiatives, and/or people
- Consulting Experience
- Databricks Machine Learning Associate or Machine Learning Professional Certification
- Snowflake SnowPro Core Certification or SnowPro Advanced: Data Scientist Certification
- Claude certification or experience to generate skills / agents for decision science tasks
- Experience with mathematical optimization and data science for Federal clients
Benefits
- Remote work within the US
Company Overview