Total Portfolio Analytics & Investment AI Strategy Lead

Northwestern Mutual•Milwaukee, WI

About The Position

Northwestern Mutual’s Investment Strategy Department seeks a Total Portfolio Analytics & Investment AI Strategy Lead to help advance total portfolio management and AI strategy for Northwestern Mutual’s General Account. The General Account is managed with a long-term, diversified investment approach that supports the company’s financial strength, policyowner value, and ability to meet long-duration insurance obligations. This role is responsible for building and owning the analytics strategy, data, and decision-support tools that power total portfolio management across the approximately $327 billion General Account. The role integrates data from public and private investments, external managers, and internal strategies to deliver insights on asset allocation, risk, liquidity, performance, and rebalancing. The ideal candidate blends deep financial market knowledge with strong technical capabilities in data engineering, analytics, and AI/ML, and will partner with investment leaders, engineering, technology, and cross-enterprise teams to drive scalable capabilities forward.

Requirements

Bachelor’s degree in a quantitative field, such as Finance, Economics, Statistics, Computer Science, or Engineering, is required
7-10+ years of relevant experience in multi-asset or total portfolio analytics within asset management, an allocator, or consulting, with exposure to both public and private markets.
Experience leading, managing, or mentoring a small team, with the ability to set priorities, coach team members, and create accountability for delivery.
Strong understanding of asset allocation, portfolio construction, risk management, liquidity management, performance measurement, and attribution across asset classes.
Proficiency in SQL and Python, or R, with experience working with modern data warehousing platforms such as Snowflake or Redshift.
Experience building dashboards, analytical tools, and decision-support applications using BI tools such as Tableau, Power BI, Streamlit.
Familiarity with investment platforms such as Aladdin, Bloomberg, eFront, Yardi, or other investment data, risk, performance, and portfolio management systems.
Familiarity with machine learning, AI-enabled workflows, model governance, prompt-based tooling, agents, or production analytics environments.
Ability to partner with engineering, technology, risk, compliance, data governance, and enterprise AI teams to move new capabilities through intake, approval, delivery, and adoption.
Ability to distill complex analysis into clear narratives and recommendations for both technical and non-technical audiences.
Structured thinker with strong analytical rigor; comfortable operating in ambiguity and balancing strategic and tactical priorities.
High ownership, curiosity, and bias for action; collaborative and adaptable with a strong commitment to quality and controls.

Nice To Haves

advanced degree, such as an MS or PhD, or progress toward the CFA designation is a plus.

Responsibilities

Lead total portfolio analytics strategy: Build and maintain the analytical frameworks that support strategic and tactical asset allocation, portfolio construction, liquidity planning, risk budgeting, rebalancing, and scenario analysis across public and private assets.
Drive Institutional Investments AI strategy and adoption: Identify, prioritize, and lead AI-enabled opportunities that improve investment decision-making, automate repeatable workflows, and create scalable capabilities for the broader investment organization.
Partner with engineering and enterprise teams: Work closely with engineering, data, technology, risk, compliance, and enterprise AI partners to translate investment needs into technical requirements, align delivery priorities, and move capabilities from concept to production.
Build and maintain data models: Collaborate with data engineers to onboard total portfolio data sources, integrate public and private market data, design repeatable data models, and establish data quality checks, lineage, and documentation.
Develop interactive data visualizations: Create dashboards, applications, and portfolio views using Power BI, Streamlit, or similar tools to help investment leaders and portfolio teams understand exposures, performance, risk, liquidity, and capital allocation decisions.
Apply quantitative methods: Define and execute the analytics strategy and roadmap for portfolio models and decision-support tooling; examples include developing portfolio construction and optimization models for strategic and tactical asset allocation, risk budgeting, optimization under constraints, and rebalancing; build scenario and stress-testing frameworks for macro and market scenarios, liquidity stress, and commitment pacing across public and private assets; and implement performance and attribution methodologies for multi-asset attribution, exposures, factor analytics, and benchmark alignment.
Improve existing systems and workflows: Identify gaps in current investment data, analytics, AI, and reporting processes; help prioritize improvements that reduce manual work, improve controls, and speed time to decision.
Manage and develop talent: Serve as a people leader for a small team of analysts, data professionals, or associates by setting priorities, coaching team members, creating clear accountability, and helping the team deliver against department objectives.
Collaborate effectively: Work closely with portfolio managers, investment strategy leaders, traders, operations, risk, data, engineering, and enterprise partners to advance total portfolio capabilities and enable better investment decisions.