Talent
***OPEN TO CHICAGO, SCOTTSDALE, and/or SAN FRANCISCO***
***US Citizens or Green Card holders ONLY***
Company Overview Our client is a U.S.-based financial technology organization that builds and operates large-scale, real-time data and analytics platforms supporting critical financial services across the country. The company leverages advanced data, machine learning, and cloud technologies to power high-volume, mission-critical systems used every day at national scale.
With a strong engineering culture and long-term investment in technology, the organization offers the stability of an established enterprise alongside the pace and innovation of a modern fintech. Teams work collaboratively to solve complex, high-impact problems where performance, reliability, and trust are essential.
This is an opportunity to join a respected organization where technology is central to the business and engineers have the resources, ownership, and support to build production-grade systems that make a real-world impact.
Role Overview This role serves as a core member of a data science team within a large, data-driven organization, delivering advanced machine learning and artificial intelligence solutions end to end. The position partners closely with senior technical and business stakeholders to understand complex problems, explore and aggregate data, develop and validate models, and deploy solutions that drive meaningful business outcomes.
Key Responsibilities
Independently explore, aggregate, and analyze large and complex datasets to identify patterns, anomalies, and data quality issues that impact model performance
Perform end-to-end feature engineering, including ideation, creation, validation, and selection
Design, build, and deploy machine learning models from concept through production
Write production-quality, maintainable code in a fast-paced, collaborative environment
Solve complex analytical problems using large-scale (terabyte-level) datasets
Apply a range of machine learning techniques to identify optimal approaches for business problems
Partner with Product and Engineering teams to identify trends, opportunities, and scalable solutions
Communicate insights, model results, and performance clearly to non-technical audiences using visualizations and storytelling
Support organizational commitments to system integrity, data security, and confidentiality
Minimum Qualifications
Bachelor’s degree in Mathematics, Statistics, Computer Science, Operations Research, or a related field
4+ years of professional experience in data science, engineering, applied mathematics, or a related role
Experience developing data science pipelines and workflows using Python, R, or similar languages
Strong SQL skills, including performance tuning and working with large datasets
Hands-on experience applying machine learning techniques and understanding key parameters affecting model performance
Experience with common ML libraries (e.g., scikit-learn, MLlib, or similar)
Experience with data visualization tools and techniques
Ability to write well-structured, production-level code that is readable and explainable
Strong communication skills, with the ability to translate complex analyses into actionable insights for non-technical stakeholders
Ability to pass background and drug screening
Preferred Qualifications
Master’s or PhD in a quantitative field (advanced degree preferred)
Advanced knowledge of machine learning algorithms and modeling techniques
2+ years of industry experience focused on applied machine learning
Demonstrated ability to work effectively in ambiguous, fast-moving environments
Strong curiosity and experience exploring data to uncover non-obvious patterns
Interest in rapid prototyping, experimentation, and proof-of-concept development
This role is primarily office-based and sedentary, requiring extended use of a computer and the ability to communicate effectively with internal and external partners. Reasonable accommodations may be made to enable individuals to perform essential job functions.
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***US Citizens or Green Card holders ONLY***
Company Overview Our client is a U.S.-based financial technology organization that builds and operates large-scale, real-time data and analytics platforms supporting critical financial services across the country. The company leverages advanced data, machine learning, and cloud technologies to power high-volume, mission-critical systems used every day at national scale.
With a strong engineering culture and long-term investment in technology, the organization offers the stability of an established enterprise alongside the pace and innovation of a modern fintech. Teams work collaboratively to solve complex, high-impact problems where performance, reliability, and trust are essential.
This is an opportunity to join a respected organization where technology is central to the business and engineers have the resources, ownership, and support to build production-grade systems that make a real-world impact.
Role Overview This role serves as a core member of a data science team within a large, data-driven organization, delivering advanced machine learning and artificial intelligence solutions end to end. The position partners closely with senior technical and business stakeholders to understand complex problems, explore and aggregate data, develop and validate models, and deploy solutions that drive meaningful business outcomes.
Key Responsibilities
Independently explore, aggregate, and analyze large and complex datasets to identify patterns, anomalies, and data quality issues that impact model performance
Perform end-to-end feature engineering, including ideation, creation, validation, and selection
Design, build, and deploy machine learning models from concept through production
Write production-quality, maintainable code in a fast-paced, collaborative environment
Solve complex analytical problems using large-scale (terabyte-level) datasets
Apply a range of machine learning techniques to identify optimal approaches for business problems
Partner with Product and Engineering teams to identify trends, opportunities, and scalable solutions
Communicate insights, model results, and performance clearly to non-technical audiences using visualizations and storytelling
Support organizational commitments to system integrity, data security, and confidentiality
Minimum Qualifications
Bachelor’s degree in Mathematics, Statistics, Computer Science, Operations Research, or a related field
4+ years of professional experience in data science, engineering, applied mathematics, or a related role
Experience developing data science pipelines and workflows using Python, R, or similar languages
Strong SQL skills, including performance tuning and working with large datasets
Hands-on experience applying machine learning techniques and understanding key parameters affecting model performance
Experience with common ML libraries (e.g., scikit-learn, MLlib, or similar)
Experience with data visualization tools and techniques
Ability to write well-structured, production-level code that is readable and explainable
Strong communication skills, with the ability to translate complex analyses into actionable insights for non-technical stakeholders
Ability to pass background and drug screening
Preferred Qualifications
Master’s or PhD in a quantitative field (advanced degree preferred)
Advanced knowledge of machine learning algorithms and modeling techniques
2+ years of industry experience focused on applied machine learning
Demonstrated ability to work effectively in ambiguous, fast-moving environments
Strong curiosity and experience exploring data to uncover non-obvious patterns
Interest in rapid prototyping, experimentation, and proof-of-concept development
This role is primarily office-based and sedentary, requiring extended use of a computer and the ability to communicate effectively with internal and external partners. Reasonable accommodations may be made to enable individuals to perform essential job functions.
#J-18808-Ljbffr