Your Team Responsibilities
MSCI’s Analytics Factor Model team, part of the broader Production Shared Services (PSS) organization, is seeking a Risk Model Analytics Associate to support the design, testing, delivery, and quality assurance of equity risk and factor models.
This role sits at the intersection of quantitative analysis, business analysis, and AI-driven transformation — contributing to projects that improve how models are built, validated, distributed, and monitored.
The ideal candidate brings a strong analytical foundation, technology curiosity, and the ability to translate complex model and data concepts into actionable outcomes across the project and production lifecycle.
You will collaborate closely with quantitative researchers, engineers, and product teams across global offices to accelerate time-to-market, ensure data integrity, and drive operational improvements through emerging AI tools and methodologies.
Your Key Responsibilities
Project & Business Analysis
Work with quantitative researchers, engineers, and product teams to gather, document, and validate business and functional requirements for risk and factor model initiatives.
Design and execute test cases to verify that model outputs, data pipelines, and system behavior meet specifications — serving as a key bridge between quant research and engineering.
Translate complex model and data concepts into clear documentation, user stories, and project artifacts for both technical and non-technical stakeholders.
Leverage AI tools to assist in requirements synthesis, documentation generation and stakeholder communication – accelerating the analytical and documentation lifecycle
Monitor project milestones and deliverables, flagging risks and dependencies to ensure on-time model delivery to clients.
Participate in Agile/SAFe PI Planning cycles, contributing to sprint goals and team-level objectives – with the opportunity to grow into a Scrum Master or Product Owner capacity over time
Quality Assurance & Model Validation
Conduct in-depth data analysis to investigate model discrepancies, output anomalies, and data quality issues — going beyond surface-level checks to understand root causes.
Build analytical tools and scripts (e.g., Python, SQL) to automate and accelerate QA workflows, reducing manual effort and improving consistency.
Apply LLM-based and agentic frameworks to enhance QA workflows – including automated anomaly flagging, intelligent triage of model discrepancies, and pattern detection across large datasets
Develop and maintain validation frameworks that support repeatable, scalable quality processes across model production and distribution.
Contribute to the team’s MCP tooling initiatives, expanding analyst access to model repositories, databases, and APIs for more interactive and scalable QA coverage
Collaborate with operations and engineering teams to close data integrity gaps and continuously raise the quality bar.
AI & Process Transformation
Actively participate in and contribute to building AI-driven solutions aimed at improving operational efficiency and accelerating model delivery to clients.
Prototype and develop AI-assisted tools — including LLM-based workflows, intelligent monitoring systems, and automation pipelines — within the analytics operations context.
Identify operational pain points and translate them into concrete, deliverable improvement initiatives.
Partner with team leads and global counterparts to pilot, refine, and scale AI and automation solutions across the PSS organization.
Your Skills and Experience That Will Help You Excel
Minimum Qualifications
Bachelor’s degree in Finance, Economics, Mathematics, Statistics, Computer Science, Engineering, or a related quantitative field.
At least 3 years of experience in a data analysis, business analysis, quantitative research, or analytics operations role – with demonstrated exposure to or application of AI tools, LLMs, or automation frameworks as part of day-to-day analytical work
Strong quantitative and analytical skills — comfortable working with large datasets, model outputs, and numerical validation.
Proficiency in Python and SQL for data analysis, scripting, and workflow automation
Demonstrated ability to use AI tools to enhance analytical work – whether for data exploration, pattern detection, automated reporting, or operational triage.
Familiarity with AI/LLM-based tools applied to analytics workflows – such as automated QA, anomaly flagging, insight generation.
Strong written and verbal communication skills in English — able to convey technical concepts clearly to diverse stakeholders.
Demonstrated ability to manage multiple workstreams and deliver in a project-based, cross-functional environment.
Preferred Qualifications
Exposure to financial models, quantitative analysis, or risk-related work in an academic or professional setting.
Understanding of statistical and mathematical concepts and their practical application — such as regression, time-series analysis, or probability-based methods.
Experience working with numerical data at scale — cleaning, transforming, and deriving insights from complex datasets using libraries such as NumPy and pandas
Experience with agentic AI frameworks such as LangChain or Model Context Protocol (MCP) integration
Experience building internal tools, scripts, or utilities that solve real operational problems.
Hands-on experience with or strong interest in AI tools, LLMs, or automation frameworks.
Familiarity with Agile/SAFe delivery frameworks — sprints, PI Planning, JIRA.
Experience with cloud data platforms such as Snowflake or equivalent.
Master’s degree in a quantitative or technical field is a plus.

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