Data Scientist

This role supports a financial services organization by applying advanced data science and machine learning techniques to solve complex business problems using large-scale datasets.

The position focuses on end-to-end feature engineering, model development, and writing production-quality code in a fast-paced, collaborative environment.

The individual partners closely with product and engineering teams to uncover trends, improve algorithm performance, and drive data-informed decisions.

Key ResponsibilitiesIndependently analyze and aggregate large, complex datasets to identify anomalies that affect model and algorithm performanceOwn the full lifecycle of feature engineering, including ideation, development, validation, and selectionDevelop and maintain production-quality code in a fast-paced, agile environmentSolve challenging analytical problems using extremely large (terabyte-scale) datasetsEvaluate and apply a range of machine learning techniques to determine the most effective approach for business use casesCollaborate closely with product and engineering partners to identify trends, opportunities, and data-driven solutionsCommunicate insights, results, and model performance clearly through visualizations and explanations tailored to non-technical stakeholdersAdhere to established standards and practices to ensure the security, integrity, and confidentiality of systems and data Minimum QualificationsBachelor’s degree in Mathematics, Statistics, Computer Science, Operations Research, or a related fieldAt least 4 years of professional experience in data science, analytics, engineering, or a closely related disciplineHands-on experience building data science pipelines and workflows using Python, R, or similar programming languagesStrong SQL skills, including query development and performance tuningExperience working with large-scale, high-volume datasets (terabyte-scale)Practical experience applying a variety of machine learning methods and understanding the parameters that impact model performanceFamiliarity with common machine learning libraries (e.g., scikit-learn, Spark ML, or similar)Experience with data visualization tools and techniquesAbility to write clean, maintainable, and production-ready codeStrong interest in rapid prototyping, experimentation, and proof-of-concept developmentProven ability to communicate complex analytical findings to non-technical audiencesAbility to meet standard employment screening requirements