Principal Associate, Data Scientist

Jobtailor; Illinois; On-site; USD 130,000 - 170,000; Full time

Job summary

Jobtailor seeks a data science leader to partner with data scientists, engineers, and product managers to deliver ML-powered products customers love. You will leverage Python, Conda, AWS, H2O, and Spark to extract insights from large data volumes and build robust models from design through deployment.

You will lead and develop a team, ensure model risk governance, and apply GenAI alongside traditional ML approaches while documenting processes and maintaining production solutions.

Qualifications

• Bachelor's degree in a quantitative field or higher.
• 5 years of experience performing data analytics (or 3 years with Master’s or PhD).
• At least 1 year of experience with AWS.
• At least 3 years' experience in Python, Scala, or R.
• At least 3 years' experience with machine learning.
• At least 3 years' experience with SQL.
• Experience with big data and distributed computing (Spark or equivalent).
• Experience with model risk governance and leading a team.
• Experience with GenAI techniques and traditional ML model development.

Responsibilities

• Partner with cross-functional teams to deliver ML-powered products customers love.
• Leverage Python, Conda, AWS, H2O, Spark to reveal insights from large data sets.
• Build ML models from design through training, evaluation, and deployment; monitor and maintain models.
• Create production-ready data science solutions with clear documentation and best practices.

Skills

Machine Learning

Python Programming

Data Analytics

SQL

Team Leadership

Communication

GenAI Techniques

Model Risk Governance

Education

Bachelor's Degree in a quantitative field

Tools

AWS

H2O

Conda

Spark

Job description

• Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love
• Leverage a broad stack of technologies - Python, Conda, AWS, H2O, Spark, and more - to reveal insights hidden within large volumes of numeric and textual data
• Build machine learning models from design through training, evaluation, validation, and implementation, monitoring and supporting continuous model deployment and maintenance
• Collaborate on the design and maintenance of production data science solutions, including writing clear technical documentation and ensuring models adhere to software development best practices
• Manage model risk and maintain regulatory compliance across the model lifecycle

Requirements

• Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or higher
• 5 years of experience performing data analytics (or 3 years with Master's degree or 3 years with PhD)
• At least 1 year of experience working with AWS
• At least 3 years' experience in Python, Scala, or R
• At least 3 years' experience with machine learning
• At least 3 years' experience with SQL
• Experience with big data and distributed computing, using Spark or another comparable framework
• Experience with model risk governance
• Experience technically leading and developing a team
• Experience with both traditional machine learning and emerging GenAI techniques, focusing primarily on traditional ML model development

Core Competencies

Demonstrates expertise in building and deploying machine learning models, leveraging technologies such as Python and AWS, while ensuring compliance and managing model risk. Collaborates effectively with cross-functional teams to deliver data-driven insights and maintain production data science solutions.

Highest-signal resume keywords

• Machine Learning Model Development
• Python Programming
• AWS Experience
• Data Analytics
• Model Risk Governance

ATS Optimization Keywords

Hard Skills

• Machine Learning
• Data Analytics
• Python
• SQL
• Spark
• Scala
• R
• Model Risk Management
• GenAI Techniques
• Technical Documentation

Soft Skills

• Collaboration
• Team Leadership
• Communication

Industry Keywords

• Quantitative Field
• Regulatory Compliance
• Big Data
• Distributed Computing

Tools & Technologies

• AWS
• H2O
• Conda
• Spark