Data Scientist Job at Talent, Chicago, IL

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  • Talent
  • Chicago, IL

Job Description

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

  • 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

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