Senior Risk Model Operations & Analytics Lead

MSCI Inc.; Philippines; On-site; PHP 1,200,000 - 2,400,000; Full time

Benefits offered by this job

Global exposure

Flexible work

Learning opportunities

Inclusive culture

Job summary

MSCI invites a Senior Associate for Risk Model Analytics Operations to own the production lifecycle of multi-asset class factor models and risk data. You will supervise QA, data validation, incident management, and client delivery while pushing automation and AI-driven improvements across global teams.

You’ll collaborate with Research, Model Engineering, and Data teams to ensure timely, accurate model outputs, scalable runbooks, and SLA-adherent distribution across channels.

Qualifications

• Bachelor's degree in Finance, Economics, Mathematics, Statistics, Computer Science, Engineering, or a related quantitative field.
• Minimum 7 years of relevant experience in data operations, production support, or a data-intensive financial services role.
• Demonstrated experience in operational transformation — proactively identifying inefficiencies, optimizing workflows, and driving automation and AI adoption rather than simply maintaining the status quo.
• Strong proficiency in Python and SQL for data analysis, investigation, and workflow automation.
• Demonstrated ability to manage and resolve production incidents end-to-end — from triage to root cause and resolution.
• Solid understanding of data pipelines, model distribution workflows, and operational quality standards.
• Demonstrated ability to use AI tools to enhance operational and analytical work
• Strong written and verbal communication skills in English — able to communicate clearly with both technical teams and client-facing stakeholders.
• Ability to manage multiple priorities under time pressure in a globally distributed team environment.

Responsibilities

• Own and execute structured QA checks on model outputs and data pipelines — understand root causes and data behavior.
• Drive QA enhancements including anomaly detection, reduction of false positives, and relevant QA rules for signal quality.
• Build SQL scripts and Python notebooks to automate recurring QA tasks and improve consistency.
• Investigate data discrepancies with Model Engineering and Research, engaging on complex model and data issues.
• Leverage AI tools to enhance QA workflows — automated anomaly flagging and agentic workflows via MCP tooling.
• Own daily production oversight for multi-asset class models — ensure runs complete accurately and issues are caught before impacting clients.
• Manage incidents end-to-end — triage, root cause analysis, stakeholder communication, post-incident docs.
• Maintain and improve runbooks, escalation procedures, and SOPs to reduce time-to-resolution and prevent recurrence.
• Oversee timely distribution of factor model data to internal/external clients across channels.
• Coordinate client delivery milestones, manage exceptions, and ensure SLA adherence for model packages.
• Serve as operational contact for client delivery queries across global counterparts.
• Identify and eliminate inefficiencies in production workflows — drive automation and smarter workflows.
• Champion AI/tooling adoption within the team — evaluate and manage AI tools and MCP integration.
• Build and maintain metrics dashboards surface actionable insights; define data to capture and why.
• Drive knowledge transfer, cross-training, and documentation for operational resilience.

Skills

Python

SQL

QA

Incident mgmt

Automation

AI tools

Communication Eng

Operational excellence

Education

Bachelor's in quantitative field

Tools

Snowflake

LangChain

MCP

ServiceNow

Jira

Job description

MSCI invites a Senior Associate for Risk Model Analytics Operations to own the production lifecycle of multi-asset class factor models and risk data. You will supervise QA, data validation, incident management, and client delivery while pushing automation and AI-driven improvements across global teams.

You’ll collaborate with Research, Model Engineering, and Data teams to ensure timely, accurate model outputs, scalable runbooks, and SLA-adherent distribution across channels.