Junior Analyst - AI Initiativees

StepStone Group•La Jolla, CA

21h•Hybrid

About The Position

As a Junior Analyst on the AI Initiatives team, you will support the development, testing, and operationalization of AI-driven data pipelines that enhance portfolio monitoring and private-markets analytics. This is an early-career role ideal for someone who enjoys hands-on data work, problem-solving, and learning how AI can be applied in private-markets investing. You will be instrumental in the analytical core of the team, ensuring our AI/ML models are accurate, efficient, and deliver maximum insight.

Requirements

Bachelor’s or Master’s degree in a quantitative discipline (e.g., Finance, Economics, Statistics, Mathematics, Computer Science, Data Science) or related field.
0-2 years of relevant experience (e.g., internships, research, coding projects).
Foundational proficiency in Python.
Familiarity with Excel, Microsoft Apps, and Git.
Strong attention to detail, ability to critically check work, and commitment to data integrity.
Solid written and verbal communication skills; comfortable translating technical concepts for non-technical stakeholders.
Proactive, curious, collaborative mindset; ability to operate in a fast-paced environment and support team goals.
Willingness to learn and grow, asking questions and taking ownership of tasks.

Nice To Haves

Coursework or project experience in machine learning, statistics, or data modelling.
Prior internship or project experience in finance, consulting or data analytics.
Basic understanding of private markets (private equity, infrastructure, secondaries) and investment terminology.

Responsibilities

Assist in running, analyzing, and improving the performance of existing AI/ML pipelines.
Assist in complex data structuring challenges by applying cleaning and transformation techniques across heterogeneous inputs, including large-scale database extracts (SQL), proprietary documents, and legacy data in Excel, to ensure maximum data integrity for AI initiatives.
Design and execute backtests and scenario analyses based on senior team hypotheses to stress-test model robustness and quantify potential investment impact.
Collaborate closely with the engineering team by testing the integration of model outputs into internal tools.
Document specific data and format requirements necessary for operationalizing new AI features.
Actively seek opportunities to streamline workflow efficiency and reduce latency in the AI pipeline, taking the lead on automating key analytical and reporting components.
Write sophisticated SQL queries.