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 Responsibilities
- Independently analyze and aggregate large, complex datasets to identify anomalies that affect model and algorithm performance
- Own the full lifecycle of feature engineering, including ideation, development, validation, and selection
- Develop and maintain production-quality code in a fast-paced, agile environment
- Solve challenging analytical problems using extremely large (terabyte-scale) datasets
- Evaluate and apply a range of machine learning techniques to determine the most effective approach for business use cases
- Collaborate closely with product and engineering partners to identify trends, opportunities, and data-driven solutions
- Communicate insights, results, and model performance clearly through visualizations and explanations tailored to non-technical stakeholders
- Adhere to established standards and practices to ensure the security, integrity, and confidentiality of systems and data
Minimum Qualifications
Bachelors 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