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About This Role
Job Description
The Wealth Management (WM) Chief Data Office (CDO) sits within the WM Risk organization and strives to find the right balance between risk management and business enablement. WM CDO’s mission is to: prevent unauthorized access to or misuse of client sensitive data and assets; abide by relevant privacy laws and regulations; effectively retain, retrieve, and protect information and records at the Firm; and mitigate risks caused by inaccurate, untimely, or incomplete WM data. The External Fraud Risk Team within WM CDO works to define appropriate fraud risk thresholds for WM and govern controls that keep net external fraud losses within tolerance while achieving business objectives.
Role Description:
The External Fraud Risk Team seeks a Vice President to support the inventory, review, and continuous monitoring of WM’s fraud rules. This individual will serve as a senior subject matter expert on fraud rules, use performance data to continually optimize rules to balance fraud risk with client friction, and build an automated, data\-driven rule inventory that tracks rule efficacy, coverage, effectiveness, and client friction. Additionally, they will be the product owner of a new technology platform that continually tests fraud rules for implementation issues. They will ensure this new platform and WM External Fraud Risk’s test cases stay ahead of the ever\-evolving fraud landscape and support the launch of new crypto, digital asset, and banking and lending products.
Key Responsibilities:
- Designing and building an automated rule inventory that allows WM External Fraud Risk to quickly and accurately answer questions about fraud rules
- Identifying data sources and building analytical models to evaluate the trade\-offs between fraud rule/control effectiveness and client friction
- Performing gap analyses of fraud rules and ensuring rule enhancements and optimizations continue to align with WM’s initiatives to roll out new products and services
- Serving as the primary business owner of WM External Fraud Risk’s fraud rules testing platform, accountable for business requirements, platform governance, and ongoing enhancements to support effective fraud rule testing
- Project managing the implementation of WM External Fraud Risk’s fraud rules testing platform, ensuring the initial product and continued enhancements are delivered on time and within budget
- Designing and executing a process for fraud rules continuous monitoring; use WM External Fraud Risk’s fraud rules testing platform as the vehicle to identify, confirm, and remediate fraud rule gaps
- Building and tuning test cases and synthetic datasets as the fraud landscape and client behaviors evolve to ensure rules are continuing to function as intended
- Implementing metrics that allow senior management to track the performance of WM External Fraud Risk’s fraud rules testing platform as well as fraud rule performance, efficacy, and client friction
Qualifications:
- 5\-10 years of relevant experience
- Expertise in fraud rule implementations that balance fraud risk with business enablement and client friction
- Extensive background in statistics and/or data science
- Proven track record of understanding and organizing large amounts of data to calculate performance statistics; ability to interpret results and document conclusions for both technical and non\-technical audiences
- Ability to design business\-facing metrics that drive control enhancement proposals to senior management
- Experience performing gap analyses of fraud rules and documenting results backed by data that hold up to scrutiny
- Proven track record of architecting technology platforms that require complex system and data integrations
- Strong understanding of SDLC with extensive experience in designing and performing software QA testing
- Ability to build analytics and automations using tools like Generative AI, Python, SQL, and Dataiku
- Project management experience in a highly matrixed organization with multiple stakeholders
- Exceptional critical thinking, problem\-solving, and research skills
- Comfort challenging and escalating risks and decisions
- Excellent written and verbal communication skills, with the ability to communicate at all levels within the organization
- Ability to independently manage and execute on multiple, simultaneous workstreams and exhibit strong attention to detail
Preferred Qualifications:
- Degree (or equivalent experience) in Computer Science/Software Engineering
- Solid understanding of cybersecurity, network security principles, and authentication controls
- Hands\-on experience with software/code auditing or penetration testing
- Experience with incident response and root cause analysis
- Knowledge of the financial services industry; preferably in wealth management, risk management, or technology
- Experience with content and project management tools, including SharePoint, Jive, and Jira
WHAT YOU CAN EXPECT FROM MORGAN STANLEY:
At Morgan Stanley, we raise, manage and allocate capital for our clients – helping them reach their goals. We do it in a way that’s differentiated – and we’ve done that for 90 years. Our values \- putting clients first, doing the right thing, leading with exceptional ideas, committing to diversity and inclusion, and giving back \- aren’t just beliefs, they guide the decisions we make every day to do what's best for our clients, communities and more than 80,000 employees in 1,200 offices across 42 countries. At Morgan Stanley, you’ll find an opportunity to work alongside the best and the brightest, in an environment where you are supported and empowered. Our teams are relentless collaborators and creative thinkers, fueled by their diverse backgrounds and experiences. We are proud to support our employees and their families at every point along their work\-life journey, offering some of the most attractive and comprehensive employee benefits and perks in the industry. There’s also ample opportunity to move about the business for those who show passion and grit in their work.
To learn more about our offices across the globe, please copy and paste https://www.morganstanley.com/about\-us/global\-offices into your browser.
Expected base pay rates for the role will be between $115,000 and $190,000 per year at the commencement of employment. However, base pay if hired will be determined on an individualized basis and is only part of the total compensation package, which, depending on the position, may also include commission earnings, incentive compensation, discretionary bonuses, other short and long\-term incentive packages, and other Morgan Stanley sponsored benefit programs.
Morgan Stanley is an equal opportunity employer committed to building and maintaining a workforce that is diverse in experience and background. Our recruiting efforts reflect our strong commitment to a culture of inclusion, where individuals are hired, developed, and advanced based on their skills and talents.
Our workforce reflects a broad cross\-section of the global communities in which we operate, bringing a variety of backgrounds, talents, perspectives, and experiences.
For more information, please visit : https://www.morganstanley.com/people\-opportunities/eeo .
Salary Context
This $115K-$190K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Morgan Stanley, 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 Required
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 $218,750 based on 3,817 positions with disclosed compensation. This role's midpoint ($152K) sits 30% below the category median. Disclosed range: $115K to $190K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Morgan Stanley AI Hiring
Morgan Stanley has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $190K - $190K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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