Principal Data Scientist, Applied Intuition

• Master’s degree or equivalent experience in data science, operations analysis, statistics, or a related field
• Experience working with highly scalable simulation environments
• Familiarity with military domains such as combat tactics, tracking and fusion, battle management, planning, and autonomous decision-making
• Strong fundamentals in probability, statistics, modeling, and simulation
• 6+ years of hands-on experience in data science
• The ability to prototype algorithms and solutions and conduct analysis using languages and tools such as Python, R, Matlab, SQL and noSQL databases
• Willing to travel for company business between our offices, as well as to customer sites (~10%)
• Must be a U.S. Citizen
• Must hold or be eligible for a U.S. TOP SECRET/SCI security clearance
• Must be clearable for Special Access Programs

Desirable

• As a Principal Data Scientist, you will develop and maintain the data strategy for EpiSci.
• You will work with engineers and software product owners to determine what data is needed for training and evaluating autonomy components and solutions and how that data can be obtained
• You will work with tactical experts and test engineers to determine the metrics that are needed for test and evaluation, and you will develop plans to gather and incorporate these metrics into the autonomy development workflow.
• You will support testing campaigns that take autonomy solutions from simulation to system- and hardware-in-the-loop testing, then to live test.
• In collaboration with data engineers, you will find solutions for historical data management and comparison of different baselines.
• The role involves research, innovation, software prototyping, planning, documentation, and productization support
• Develop data requirements and tool maturation plans for the full scope of the company’s product roadmap
• Work with data engineers to design solutions for gathering and leveraging test and evaluation metrics, including the use of live, virtual, and constructive simulation environments
• Assist machine learning engineers in the design of model architectures and algorithms for environments that include competition, low- to high-fidelity models of flight and electromagnetic effects, and partially observable information
• Work with customer testing communities to ensure that test plans account for the generation, collection, and analysis of the data necessary to meet program objectives, including solutions for sim-to-real challenges
• Work with autonomy engineers to improve development and testing workflows through better use of simulation, data, metrics, and software tools
• Write prototype and data management code in Python 3.10+ and other languages as needed