Junior AI Analytics & Data Pipeline Specialist

StepStone Group; La Jolla Ranch (CA); Hybrid; USD 39,000 - 44,000; Full time

Benefits offered by this job

Hybrid work model

Equal Opportunity Employer

Job summary

StepStone Group in California is seeking a Junior Analyst on the AI Initiatives team to support the development and validation of AI‑driven data pipelines for private markets analytics. This early‑career role emphasizes hands‑on data work, problem solving, and learning how AI can be applied in private‑markets investing.

You will contribute to model validation, data extraction via SQL, backtesting, and integrating model outputs into internal tools, all while collaborating with engineering to

Qualifications

• Bachelor’s or Master’s degree in a quantitative discipline or related field.
• 0–2 years of relevant experience (internships, research or projects).
• Foundational proficiency in Python and familiarity with Excel, Microsoft Apps, and Git.
• Strong attention to detail and ability to communicate technical concepts to non‑technical stakeholders.

Responsibilities

• Validate and improve AI/ML data pipelines and models.
• Perform data structuring through SQL, Excel, and data transformations to ensure data integrity.
• Design backtests and scenario analyses to assess model robustness and impact on investments.
• Collaborate with engineering to operationalize AI features and document data requirements.
• Write SQL queries to source advanced data for analytics.

Skills

Python

Data analysis

SQL

Communication

Education

Bachelor’s or Master’s degree in a quantitative discipline

Tools

SQL

Excel

Git

Job description

StepStone Group in California is seeking a Junior Analyst on the AI Initiatives team to support the development and validation of AI‑driven data pipelines for private markets analytics. This early‑career role emphasizes hands‑on data work, problem solving, and learning how AI can be applied in private‑markets investing.

You will contribute to model validation, data extraction via SQL, backtesting, and integrating model outputs into internal tools, all while collaborating with engineering to