Applied Scientist (Optimization and Decision Systems) - Colorado Springs, CO

Davidson-Technologies,-Inc.; Colorado Springs (CO); On-site; USD 120,000 - 180,000; Full time

Job summary

Davidson Technologies, Inc. seeks an Applied Scientist in Colorado Springs to develop algorithms for optimization, uncertainty, simulation, and sequential decision-making in defense applications.

This self-directed role emphasizes rigorous mathematical reasoning, practical modeling, and evidence-based decision making within a team that values engineering excellence and real-world impact. You will translate ambiguous requirements into concrete models, design computational experiments, compare

Qualifications

• Experience applying quantitative methods to real-world problems.
• Proficient in Python and C++ for algorithm development.
• Familiarity with optimization, statistics, simulation, and ML methods.

Responsibilities

• Formulate problems using mathematical and computational models.
• Develop and combine algorithms from optimization, ML, and simulation.
• Improve decisions and efficiency using information over time.
• Design experiments, analyze results, and guide improvements.
• Implement algorithms and maintain software in Python and C++.
• Develop discrete-event simulations and real-time sequential decision systems.
• Collaborate with the tech lead on problem formulation and direction.

Skills

Python

C++

Optimization

Probability & Statistics

Simulation

Reinforcement Learning

Bandit Algorithms

Active Learning

Git/GitHub

Communication

Education

Bachelor's degree in mathematics, OR, statistics, CS, engineering, or related field

Master's degree or PhD in related field

Tools

Git/GitHub

Job description

Davidson is distinguished in the aerospace and missile defense industry, in part, because of its excellence in hiring exceptional experts, engineers and software developers dedicated to delivering advanced, intelligent technology solutions in defense of our nation.

Davison is seeking an Applied Scientist in Colorado Springs to develop algorithms and computational solutions for complex real-world problems involving optimization, uncertainty, simulation, and sequential decision-making.

This role combines applied mathematics, algorithm development, programming, and experimental analysis. You will work on problems that require translating ambiguous requirements into practical models, evaluating alternative approaches, and adapting methods to fit real-world constraints.

Working closely with the technical lead, you will have the autonomy to investigate problems, challenge assumptions, propose improvements, and implement solutions. Your work will span improving existing applications, developing simulation-based optimization methods, and advancing real-time decision systems that use information as it becomes available.

Job Responsibilities:

• Formulate complex problems using appropriate mathematical and computational models, making and evaluating assumptions where necessary.
• Develop, adapt, and combine algorithms from optimization, probability, statistics, machine learning, and sequential decision-making.
• Identify opportunities to improve decisions and computational efficiency by exploiting available information and information revealed over time.
• Design computational experiments, analyze results, and use evidence to evaluate approaches and guide improvements.
• Implement algorithms and contribute to maintainable software in Python and C++, including improving existing applications, testing, and code quality.
• Develop and optimize discrete-event simulations and contribute to real-time systems for sequential decision-making under uncertainty.
• Collaborate with the technical lead to assess existing approaches, identify limitations, and develop practical alternatives.

Job Requirements:

• Bachelor's degree in mathematics, operations research, statistics, computer science, engineering, or a related field, with 5+ years of relevant experience; or a Master's or PhD in a related field with 3+ years of relevant experience.
• Demonstrated experience applying quantitative, statistical, or algorithmic methods to complex real-world problems.
• Practical programming ability in Python and C++.
• Experience applying relevant methods such as optimization, probability, statistics, simulation, stochastic optimization, reinforcement learning, bandit algorithms, or active learning. Candidates need experience in some relevant areas, not all.
• Ability to critically evaluate assumptions and approaches, reason through ambiguity, and develop and test practical solutions.
• Familiarity with Git and GitHub or comparable version-control tools.
• Ability to communicate technical reasoning, results, and trade-offs clearly.

Desired (not required) competencies:

• Experience with any of the following: sequential decision-making, reinforcement learning, stochastic optimization, bandit algorithms, or active learning.
• Experience with simulation-based optimization.
• Familiarity with supervised and unsupervised learning.
• Experience with scientific computing, simulation, or computational experimentation.
• Experience improving software from prototype to maintainable application.
• Familiarity with automated testing or CI/CD practices.
• Graduate-level study or research in a relevant quantitative or computational discipline.

Relevant experience may come from professional work, research, graduate projects, or other substantial technical projects. Candidates are not expected to have expertise in every listed method.

Clearance:

Ability to obtain a DoD secret clearance

The Opportunity

This is a self-directed technical role for someone who enjoys applying their knowledge to interesting, open-ended problems. You will have room to explore approaches, question assumptions, and implement improvements while collaborating with the technical lead on problem formulation and overall direction.

The emphasis is on sound technical reasoning, practical algorithm development, and evidence-based problem-solving-not on any single academic discipline, algorithm, or software framework.