Senior Manager, AI/ML Applied Research

The Vanguard Group•Malvern, PA

20h•Hybrid

About The Position

The Senior Manager, AI/ML Applied Research leads a focused portfolio of high-priority applied AI and machine learning initiatives across Investments and Finance. The role translates consequential business and investment problems into scientifically rigorous, scalable AI capabilities and works hands-on to advance those capabilities into production systems. It sets scientific direction with senior leadership and develops and evaluates research hypotheses, model approaches, and experiments from problem formulation through validation and production transition. This player-coach leads and develops a small multidisciplinary team of researchers and engineers, provides technical direction, and establishes standards for research quality, reproducibility, explainability, documentation, and model governance. The role works with senior stakeholders to frame business and investment challenges as research questions, secure project alignment and resourcing, and communicate findings, recommendations, limitations, and business implications to senior audiences. It also hires, evaluates, and supervises employees; provides coaching and training; sets performance standards; reviews performance; and makes compensation decisions under applicable HR policies. Vanguard uses a hybrid working model for the majority of its crew members, intended to support flexibility as well as in-person learning, collaboration, and connection. Vanguard is not offering visa sponsorship for this position.

Requirements

At least 8 years of related work experience in analytical and research roles.
Bachelor’s degree (B.E./B.Tech) in Applied Mathematics, Analytics, Statistics, Economics, or Computer Science.
At least 2 years of people leadership experience managing researchers, data scientists, or professionals in similar analytical roles, including performance management, coaching, and employee development.
Programming skills to access, transform, and prepare large-scale data for statistical modeling.
Experience using statistical and machine learning methods, especially deep learning.
Experience personally developing and leading applied AI, machine learning, quantitative research, or systematic investing initiatives.
Experience providing technical direction to multidisciplinary teams of researchers, scientists, and engineers.
Experience with investment management practices, financial data, quantitative methods, and research workflows.
Experience with machine learning, statistical modeling, experimental design, back testing, and model validation.
Experience evaluating research hypotheses, alternative model approaches, methodological assumptions, and empirical evidence.
Experience guiding research from problem formulation through experimentation, validation, and production transition.
Experience establishing standards for research quality, reproducibility, explainability, documentation, and model governance.
Experience presenting technical findings, model limitations, and strategic recommendations to senior business stakeholders.

Nice To Haves

Master’s degree, PhD, or equivalent research experience in machine learning, data science, applied mathematics, or a related discipline.
Experience in investment management or quantitative finance.
Experience with AI governance and production machine learning practices.
Experience developing and validating machine learning solutions for complex problems.
Experience establishing reusable research standards, explainability practices, or enterprise AI capabilities.
Master’s degree or PhD preferred.

Responsibilities

Lead and personally drive a focused portfolio of high-priority applied AI and machine learning initiatives across Investments and Finance.
Translate business and investment problems into rigorous, scalable AI capabilities that deliver business value and transition into production systems.
Set scientific direction with senior leadership and personally develop and evaluate research hypotheses, model approaches, and experiments.
Challenge methodological assumptions and empirical evidence from problem formulation through validation and production transition.
Lead and develop a small multidisciplinary team of researchers and engineers as a player-coach, providing technical direction and working alongside the team on complex research problems.
Establish and evolve standards for research quality, reproducibility, explainability, documentation, and model governance.
Hire, evaluate, and supervise employees; provide guidance and training; set performance standards; review performance; and make compensation decisions in accordance with applicable HR policies and procedures.
Partner with senior stakeholders to frame business and investment challenges as applied research questions and develop proposals to secure alignment and resourcing.
Present research and technical findings, recommendations, and business implications to internal and external senior business leaders.
Contribute to and lead the lab’s collaborative culture of learning.
Evaluate research hypotheses, alternative model approaches, methodological assumptions, and empirical evidence.
Connect business strategy, applied research, governance, and production engineering across the AI development lifecycle.