Talent
Supports a financial services organization by applying advanced data science and machine learning techniques to solve complex business problems using large-scale datasets. Focuses on end-to-end feature engineering, model development, and production-quality code in a fast-paced, collaborative environment. 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
Bachelor’s 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
Seniority level Mid‑Senior level
Employment type Full‑time
Job function Information Technology
Industries Financial Services, IT Services, IT Consulting
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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
Bachelor’s 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
Seniority level Mid‑Senior level
Employment type Full‑time
Job function Information Technology
Industries Financial Services, IT Services, IT Consulting
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