Senior Associate, Investment Data & AI

New York Life•New York, NY

4d•Hybrid

About The Position

The Multi-Asset Solutions (MAS) team at NYLIM is building an AI-enabled investment infrastructure, starting with data accuracy and evolving into a platform that enhances research, trading, and reporting. This role is within the portfolio management team, not technology. You will collaborate directly with portfolio managers and trading operations professionals, gaining a deep understanding of the investment process. You will apply this knowledge to design data structures and AI-enabled workflows for the future. This role requires FINRA licensed and/or FINRA Associated Person pre-hire fingerprinting.

Requirements

Bachelor's degree in Computer Science, Data Science, Engineering, Statistics, Finance, or a related field.
2–6 years of experience in data analytics, data engineering, or an analytically intensive role.
Proficiency in scripting or data languages with the ability to think programmatically and work effectively with AI coding tools.
Experience working with relational databases, including schema design and working with structured data at scale.
Familiarity with cloud data platforms (AWS, Azure, or Google Cloud) and cloud data management.
Hands-on experience with AI or automation tools applied to real data problems.
Clear communication skills, able to explain data concepts to non-technical colleagues and investment requirements to engineers.

Nice To Haves

Asset management experience.
Familiarity with investment data: Bloomberg, OMS systems, custodian feeds, risk platforms.
Understanding of multi-asset portfolio concepts (positions, trades, benchmarks, attribution).
Experience modernizing or migrating legacy analytical workflows.
CFA progress or interest.

Responsibilities

Design and implement a standardized, cloud-based database architecture for all MAS workstreams (PM research, trading & operations, index management).
Define data structure standards for positions, trades, and performance data, ensuring consistency in naming, storage, and relationships.
Build data pipelines to integrate external data sources (Bloomberg, OMS, custodian feeds) with internal analytical and reporting tools.
Implement data validation controls and maintain documentation for schemas, architecture decisions, and governance standards.
Identify opportunities for AI tools to improve investment workflows in PM research, trading, and reporting, and assist in prototyping solutions.
Ensure data infrastructure is AI-ready: cloud-native, well-structured, and accessible to AI tools and natural language interfaces.
Stay updated on AI applications in investment management and share relevant insights with the team.
Contribute to the governance framework for responsible AI deployment, including documentation, auditability, and compliance.
Map daily operational processes for each portfolio, including reconciliation, trade lifecycle, performance calculation, compliance monitoring, and reporting.
Identify inconsistencies in process implementation that are not due to portfolio-specific mandates.
Propose and help implement a standardized daily operational process sequence for all portfolios.