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Job Code: 14555
Country: US
City: New York
Skill Category: Global Markets
Description:
Job Title: AI \& Innovation Prgoram Manager
Department: Global Markets
Location: New York
Corporate Title:Vice President
The pay range for this position at commencement of employment is expected to be between $180,000\-$250,000 per year\*
Company overview
Nomura is a financial services group with an integrated global network. By connecting markets East \& West, we service the needs of individuals, institutions, corporates and governments through our four business divisions: Wealth Management, Investment Management, Wholesale (Global Markets and Investment Banking) and Banking.
Driven by the insights of some 28,000 people worldwide, we put our clients at the center of everything we do, delivering unparalleled access to, from and within Asia. For further information about Nomura, visit www.nomura.com
Aon’s Benefit Index®, Nomura’s benefits rank \#1 amongst our competitors
Department Overview:
Nomura's Global Markets department provides liquidity, market insights, and execution services to clients worldwide across various asset classes, including equities, fixed income, currencies, and commodities. The team's focus on innovation and technology provides clients with access to cutting\-edge trading platforms and customized solutions. Nomura's Global Markets team specializes in market\-making, risk management, and electronic trading, with a strong global presence and reputation for exceptional service to clients. With expertise, global reach, and commitment to innovation, Nomura's Global Markets department is well\-positioned to continue driving growth and success in the financial industry.
Role Summary
We are seeking a Vice President to lead the program management of AI and innovation initiatives across Nomura's Global Markets division. This individual will sit at the intersection of technology, business strategy, and change management. The role requires someone who can operate comfortably across senior stakeholders, technologists, and front\-office practitioners to translate innovation ambition into measurable commercial impact.
This is a role with a mandate to build the AI operating model for Global Markets, balancing traditional AI/ML with Generative AI and Agentic AI to deliver scalable, reusable capabilities across trading desks.
Key Responsibilities
Strategic Program Leadership
- Own the end\-to\-end program roadmap for AI and innovation initiatives across Global Markets (Equities, FICC, and cross\-asset)
- Prioritize and sequence initiatives using a structured scoring framework (business outcome, data readiness, governance, technology integration, strategic alignment)
- Maintain a living portfolio view of all AI initiatives with clear stage\-gates, KPIs, and cost/benefit tracking
Delivery \& Execution
- Translate complex business problems into agentic AI\-driven solutions using orchestration frameworks and modular architecture, designing autonomous workflows, decision\-making agents, and reusable platform components
- Drive delivery of AI/ML use cases across the trade lifecycle (pre\-trade analytics, execution optimization, post\-trade automation, client engagement)
- Coordinate across technology, quant, operations, and front\-office teams to remove blockers and maintain velocity
- Champion experimentation and iterative development, capturing learnings to feed the broader product roadmap
Governance \& Risk
- Partner with AI governance, compliance, and model risk teams to ensure initiatives meet risk\-based tiering and approval requirements
- Embed governance into delivery workflows (platform\-embedded, pattern\-based approaches) rather than treating it as a sequential gate
Stakeholder Management \& Communication
- Act as the primary liaison between business and technical teams for AI initiatives
- Build executive reporting on initiative health, ROI, and strategic alignment
- Translate complex technical concepts into business value for senior leadership and front\-office stakeholders
Required Qualifications
- 8–12\+ years of experience in financial services, with significant exposure to Global Markets (Sales \& Trading, Equities, FICC)
- Proven track record delivering complex, large\-scale cross\-functional programs with at least 2 years in AI/GenAI or digital transformation
- Strong understanding of the front\-to\-back trade lifecycle (execution, capture, clearing, settlement)
- Experience with AI/GenAI implementations within Markets/Sales/Trading at a large financial institution
- Familiarity with AI governance frameworks, model risk management, and regulatory considerations
- Strong analytical and structured thinking; ability to build business cases, ROI models, and scorecards
- Exceptional stakeholder management skills
Nomura Leadership Behaviours
- Explore Insights \& Vision: Identify the underlying causes of problems faced by you or your team and define a clear vision and direction for the future.
- Making Strategic Decisions: Evaluate all the options for resolving the problems and effectively prioritize actions or recommendations.
- Inspire Entrepreneurship in People: Inspire team members through effective communication of ideas and motivate them to actively enhance productivity.
- Elevate Organizational Capability: Engage proactively in professional development and enhance team productivity through the promotion of knowledge sharing.
- Inclusion: Foster a culture of inclusion and psychological safety in the workplace and cultivate a "Risk Culture" (Challenge, Escalate and Respect).
- base pay offered may vary depending on multiple individualized factors, including market location, corporate and functional title and duties, job\-related knowledge and advanced degrees, skills, and experience. The total compensation package for this position may also include other elements, including a sign\-on bonus, restricted stock units, and discretionary awards in addition to a full range of medical, financial, and/or other benefits (including 401(k) eligibility and various paid time off benefits, such as vacation, sick time, and parental leave), dependent on the position offered. Details of participation in these benefit plans will be provided if an employee receives an offer of employment.
If hired in the U.S., employee will be in an “at\-will position” and the Company reserves the right to modify base salary (as well as any other discretionary payment or compensation program) at any time, including for reasons related to individual performance, Company or individual department/team performance, and market factors”.
\*\*US FINANCE ONLY\*\* Applicants f or this position in the Finance Division of NHA must be currently
authorized to work f or any employer in the United States. The Finance Division is not sponsoring or taking
over sponsorship of employment visas f or this posit ion at this time
*Nomura is an Equal Opportunity Employer*
Nearest Major Market: Manhattan
Nearest Secondary Market: New York City
Salary Context
This $180K-$250K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Nomura, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. Disclosed range: $180K to $250K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Nomura AI Hiring
Nomura has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $250K - $250K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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