Talent
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 field At least 4 years of professional experience in data science, analytics, engineering, or a closely related discipline Hands-on experience building data science pipelines and workflows using Python, R, or similar programming languages Strong SQL skills, including query development and performance tuning Experience 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 performance Familiarity with common machine learning libraries (e.g., scikit-learn, Spark ML, or similar) Experience with data visualization tools and techniques Ability to write clean, maintainable, and production-ready code Strong interest in rapid prototyping, experimentation, and proof-of-concept development Proven ability to communicate complex analytical findings to non-technical audiences Ability to meet standard employment screening requirements
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 field At least 4 years of professional experience in data science, analytics, engineering, or a closely related discipline Hands-on experience building data science pipelines and workflows using Python, R, or similar programming languages Strong SQL skills, including query development and performance tuning Experience 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 performance Familiarity with common machine learning libraries (e.g., scikit-learn, Spark ML, or similar) Experience with data visualization tools and techniques Ability to write clean, maintainable, and production-ready code Strong interest in rapid prototyping, experimentation, and proof-of-concept development Proven ability to communicate complex analytical findings to non-technical audiences Ability to meet standard employment screening requirements