Data Scientist

Cape Fox is seeking a skilled Data Scientist with a strong background in unsupervised learning, predictive analytics, and algorithm development to support a government client. This role involves designing and deploying tree-based clustering models, implementing core algorithms such as decision trees and k-nearest neighbors from scratch, optimizing data structures like distance matrices, and writing efficient, vectorized Python code. The ideal candidate will hold a Bachelor's degree in a quantitative field such as Computer Science, Applied Mathematics, or Economics, and have at least three years of hands-on experience in predictive analytics and Python development using libraries like NumPy, Pandas, Matplotlib, and Scikit-learn. Highly desirable qualifications include experience with AI/ML dashboard integration, dimensionality reduction, deep learning frameworks (TensorFlow, PyTorch), and familiarity with human biology, performance, and prior work with USAFSAM. This position is contingent upon contract award.

The salary range (or hiring range) for this exempt position has been established at: $96,000 - $120,000.

The above salary range represents the company's good faith and reasonable estimate of the range of possible compensation at the time of posting. In addition, we offer a variety of benefits including company holidays, paid time off, health insurance, dental insurance, vision insurance, life and disability insurance, tuition reimbursement, as well as 401K with company matches. This job will be posted until filled or withdrawn.

Core Duties:

• Develop, deploy, and tune novel unsupervised tree-based clustering machine learning models
• Accurately describe how several classes of algorithms work and explain exactly how a decision tree works, and be able to code it from scratch
• Accurately explain the difference between density-based and binning-based clustering
• Accurately explain how the k-nearest neighbors algorithm works
• Implement the k-nearest neighbors' algorithm, from scratch, in code (If unable to implement the naive version of the nearest neighbors, but not an optimized version, must be able to accurately describe an optimized version of k-nearest neighbors)
• Write an optimized list sorting algorithm, from scratch, in less than an hour, with no external resources
• Understand how to generate a distance matrix and demonstrate that they can minimize the size of a distance matrix in code
• Demonstrate writing vectorized code
• Obtain HIPAA training upon performance of work
• Maintain HIPPA standards and prevent data leaks