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About This Role
Overview
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- Location: Stamford, Connecticut
- Salary: 150,000\.00 \- 250,000\.00 USD Annual
About Us
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Synchrony is more than a financial services company, we’re a team of passionate innovators committed to delivering best\-in\-class solutions that support millions of customers across the U.S. With a bold focus on technology, data, and digital innovation, we create meaningful experiences that simplify lives and enable financial wellness.
When you join Synchrony, you become part of an inclusive culture where your voice matters, your growth is championed, and your work drives impactful results.
Job Description
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Job ID
2602232
Category
Technology
Date posted
07/31/2026
Role Summary/Purpose:
Help Synchrony accelerate responsible AI adoption and business impact across the enterprise. As Vice President, Head of Program Management for AI, you will lead the operating engine for the AI organization — translating AI strategy into disciplined execution across strategic initiatives, enablement programs, tooling rollouts, adoption efforts, governance forums, and cross\-functional delivery priorities.
Reporting to the Global Head of AI, you will establish and lead the program management function that gives the AI organization a single, trusted view of priorities, progress, dependencies, risks, and outcomes. You will partner closely with AI leadership, technology, product, business, risk, legal, compliance, HR, communications, and other enterprise stakeholders to ensure AI initiatives are well sequenced, well governed, and adopted at scale.
You are an enterprise program leader who brings structure to ambiguity, communicates clearly with executives and delivery teams, and knows how to turn bold strategy into measurable progress. You are equally comfortable shaping an operating model, coordinating complex portfolios, driving adoption of new capabilities, and helping teams move faster while maintaining appropriate discipline in a regulated financial services environment.
This role leads the program management function for the AI organization and is accountable for execution discipline, operating cadence, portfolio visibility, enablement coordination, adoption tracking, and executive reporting. It does not independently own AI model decisions, business case approval, technology architecture, risk acceptance, legal interpretation, or policy decisions; those remain with the appropriate accountable AI, business, technology, risk, legal, compliance, and governance owners. The role is expected to connect those owners, surface decisions, and drive the structure needed for timely, responsible execution.
Essential Responsibilities:
- Lead the AI program management function day\-to\-day — orchestrating execution across strategic AI initiatives, enablement programs, tooling rollouts, adoption efforts, and cross\-functional projects.
- Translate AI strategy into an integrated portfolio roadmap, milestone plan, dependency map, and execution rhythm that enables leadership to make timely tradeoff, sequencing, and resourcing decisions.
- Establish and run the AI portfolio operating model — including intake, prioritization, governance forums, decision rights, escalation paths, status reporting, and portfolio health reviews.
- Drive AI enablement activities across the firm, partnering with HR, learning, communications, business teams, technology, risk, legal, and compliance to coordinate training, awareness, change management, and adoption campaigns.
- Lead program execution for AI tools rollout and adoption — ensuring launch readiness, communications, training, stakeholder alignment, feedback loops, adoption measurement, and disciplined follow\-through on post\-launch improvements.
- Coordinate complex, enterprise\-wide AI initiatives that require alignment across business units, technology teams, data partners, risk, legal, compliance, procurement, communications, and other enabling functions.
- Aggregate, track, and report AI portfolio KPIs and program metrics, including progress against strategic priorities, adoption and engagement indicators, delivery health, risk, dependencies, issues, and executive decisions needed.
- Maintain an accurate, trusted program record for AI initiatives, ensuring status reflects validated reality and that outcomes, decisions, risks, and dependencies are documented clearly for leadership review.
- Develop and execute a proactive communication strategy for AI leadership, executive stakeholders, business partners, and enterprise audiences to build awareness, alignment, and momentum around AI priorities.
- Coordinate AI portfolio financials in partnership with Finance and business owners — tracking investment, spend, benefits, and resource needs against plan.
- Prepare and deliver executive\-ready reporting and briefings for the Head of AI, senior leadership, and governance forums — clearly articulating priorities, progress, adoption, risks, tradeoffs, and decisions required.
- Plan and orchestrate key AI program events, working sessions, leadership reviews, enablement forums, and adoption checkpoints, ensuring preparation, participation, clear outcomes, and disciplined follow\-through.
- Perform other duties and/or special projects as assigned
Qualifications/Requirements:
- Bachelor’s degree in a related discipline and 10\+ years of program, portfolio, transformation, or strategic initiative leadership experience; in lieu of a degree, 14\+ years of relevant experience.
- Proven track record leading large, complex, enterprise\-wide programs with multiple concurrent workstreams, senior stakeholders, and measurable business outcomes.
- Experience delivering strategic technology, data, digital, analytics, automation, AI, or enterprise transformation programs in financial services, banking, or another highly regulated industry.
- Working knowledge of AI, data, analytics, technology delivery, and change management concepts sufficient to coordinate credibly across technical, business, and control stakeholders.
- Understanding of responsible AI, model governance, risk management, privacy, legal, compliance, and operational control considerations sufficient to support disciplined execution without making accountable risk decisions.
- Experience with Agile, Scaled Agile, product operating models, and the tools delivery teams use day\-to\-day, such as Jira, SharePoint, or similar platforms.
- Excellent written and verbal communication, including executive\-level storytelling, portfolio reporting, and presentation of complex AI initiatives in clear business terms.
- Demonstrated ability to influence without direct authority, manage conflict, build consensus, and align senior stakeholders across business, technology, and control functions.
- Exceptional organization, prioritization, and portfolio management discipline, with the ability to keep a high volume of ambiguous, cross\-functional work moving toward outcomes.
- Experience managing vendors, consultants, internal centers of excellence, or shared enablement resources, including scope, deliverables, timelines, and outcomes.
- Comfort operating amid complexity and ambiguity; able to create structure, drive decisions, and maintain momentum in a fast\-evolving AI landscape.
- Ability and flexibility to travel for business as required
Desired Characteristics:
- Experience supporting AI, generative AI, machine learning, data science, analytics, automation, or emerging technology programs is strongly preferred.
- Experience driving enterprise change management, enablement, training, communications, and adoption for new technology capabilities or ways of working.
- Experience standing up or running a program management, portfolio management, transformation, or center\-of\-excellence operating model from the ground up.
- Proficiency with portfolio, program, adoption, and executive reporting tools, such as Microsoft Project, Planner, SharePoint, Power BI, Jira, or similar platforms.
Grade/Level: 14
The salary range for this position is 150,000\.00 \- 250,000\.00 USD Annual and is eligible for an annual bonus based on individual and company performance.
Actual compensation offered within the posted salary range will be based upon work experience, skill level or knowledge.
Salaries are adjusted according to market in CA, NY Metro and Seattle.
Our Way of Working:
We’re proud to offer you flexibility. At Synchrony, our way of working allows you to have the option to work from home near one of our Hubs or come into one of our offices. You will be required to commute to your nearest Hub (either virtual or physical) for in\-person engagement activities such as regular business or team meetings, training and culture events.
- Field Sales and some Commercial team roles may have varied location requirements based upon partner obligations or preferences.
Eligibility Requirements:
- You must be 18 years or older
- You must have a high school diploma or equivalent
- You must be willing to take a drug test, submit to a background investigation and submit fingerprints as part of the onboarding process
- You must be able to satisfy the requirements of Section 19 of the Federal Deposit Insurance Act.
- New hires (Level 4\-7\) must have 9 months of continuous service with the company before they are eligible to post on other roles. Once this new hire time in position requirement is met, the associate will have a minimum 6 months’ time in position before they can post for future non\-exempt roles. Employees, level 8 or greater, must have at least 18 months’ time in position before they can post. All internal employees must consistently meet performance expectations and have approval from your manager to post (or the approval of your manager and HR if you don’t meet the time in position or performance expectations).
Legal authorization to work in the U.S. is required. We will not sponsor individuals for employment visas, now or in the future, for this job opening. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.
Our Commitment:
When you join us, you’ll be part of an inclusive culture where your individual skills, experience, and voice are not only heard – but valued. Together, we’re building a future where we can all belong, connect, and turn ideals into action. More than 50% of our workforce is engaged in our Employee Resource Groups (ERGs), where community and passion intersect to offer a safe space to learn and grow.
This starts when you choose to for a role at Synchrony. We ensure all qualified applicants will receive consideration for employment without regard to age, race, color, religion, gender, sexual orientation, gender identity, national origin, disability, or veteran status. We’re proud to have an award\-winning culture for all.
Reasonable Accommodation Notice:
Federal law requires employers to provide reasonable accommodation to qualified individuals with disabilities. Please tell us if you require a reasonable accommodation to* for a job or to perform your job. Examples of reasonable accommodation include making a change to the application process or work procedures, providing documents in an alternate format, using a sign language interpreter, or using specialized equipment.
- If you need special accommodations, please call our Career Support Line so that we can discuss your specific situation. We can be reached at 1\-866\-301\-5627\. Representatives are available from 8am – 5pm Monday to Friday, Central Standard Time
Job Family Group:
Information Technology
Salary Context
This $150K-$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 Synchrony, 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 $214,900 based on 6,420 positions with disclosed compensation. This role's midpoint ($200K) sits 7% below the category median. Disclosed range: $150K 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.
Synchrony AI Hiring
Synchrony has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Stamford, CT, US. Compensation range: $250K - $250K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
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.
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