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

Davidson Technologies • Colorado Springs, CO

3d

About The Position

Davidson 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. The role combines applied mathematics, algorithm development, programming, and experimental analysis. It involves translating ambiguous requirements into practical models, evaluating alternative approaches, and adapting methods to real-world constraints.

Working closely with the technical lead, the Applied Scientist will investigate problems, challenge assumptions, propose improvements, and implement solutions. The work includes improving existing applications, developing simulation-based optimization methods, and advancing real-time decision systems that use information as it becomes available. This is a self-directed technical role focused on sound technical reasoning, practical algorithm development, and evidence-based problem-solving.

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. Experience in some relevant areas is sufficient; candidates are not expected to have expertise in every listed method.
• 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.
• Ability to obtain a DoD secret clearance.

Nice To Haves

• Experience with 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.

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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level