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JPMorgan Chase10K+ employees

Applied AI/ML Modeling - Vice President

NYSalary not listedPosted yesterday

Immigration summary

Visa sponsorship

Highly likelyHigh confidence

This employer sponsors this kind of work repeatedly, and is still filing this year.

3,104 recent H-1B filings · 37 similar-role filings

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Green card sponsorship

Strong historyHigh confidence

This employer has recently sponsored green cards at scale.

467 recent certified PERM filings · 3 similar-role filings

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Job description

Our Consumer Bank AI Modeling team develops advanced analytics and machine learning solutions that inform high-impact decisions across field workforce effectiveness, customer engagement, and banker-led growth.

As an Applied AI Modeling Vice President in the Consumer Bank AI Modeling team, you will build and deploy advanced AI/ML models that measurably improve banker sales effectiveness and customer outcomes. Your models will help bankers deliver the right outreach at the right time to our customers, driving deposit growth, increasing customer retention, and strengthening relationships. You will operate in a highly governed environment and partner closely with product, UX, operations, and technology teams to translate modeling innovation into field-ready tools that bankers trust and adopt.

Job responsibilities

  • Develop and launch AI/ML models that solve complex, ambiguous business problems in Consumer Banking, with emphasis on sales effectiveness and banker enablement (e.g., lead scoring, propensity modeling, next-best-action/next-best-offer, customer prioritization, retention, and cross-sell) using techniques such as deep learning, causal inference, contextual bandits, reinforcement learning, and constrained optimization.
  • Lead modeling engagements end-to-end, including scoping use cases with business partners, defining success metrics (incrementality, ROI, adoption), building project plans, and working with large, complex datasets to formulate testable hypotheses.
  • Translate model outputs into clear, actionable recommendations for non-technical partners, and produce narratives that drive adoption (why this lead, why now, what action, expected outcome).
  • Partner with governance, risk, and controls teams to expedite fair and thorough model reviews, document model intent and limitations, monitor performance and drift, and maintain adherence to regulatory and model risk management standards.

Required qualifications, capabilities, and skills

  • Master's or Ph.D. in a quantitative discipline such as Computer Science, Statistics, Machine Learning, Econometrics, Operations Research, Applied Mathematics, or a related field.
  • 4+ years of hands-on, relevant industry experience developing and deploying AI/ML models in production, including statistical modeling and modern Machine Learning.
  • Proficient in Python with hands-on experience in ML/deep learning frameworks (TensorFlow, PyTorch) and core libraries (NumPy, Scikit-Learn, Pandas). Strong working knowledge of notebooks and cloud-based development/compute.
  • Deep expertise in at least one of the following, with meaningful exposure to at least one other, recommendation/decisioning systems (next-best-action/offer), ranking, and constrained optimization, causal inference and uplift / treatment effect modeling for targeted interventions, online learning approaches (contextual bandits, multi-armed bandits, reinforcement learning), behavioral modeling and human-in-the-loop systems that drive adoption and performance, and demonstrated ability to communicate complex modeling concepts clearly to non-technical stakeholders and drive decisions.

Preferred qualifications, capabilities, and skills

  • Ph.D. in a relevant discipline.
  • Experience developing advanced AI/ML models in consumer finance, fintech, retail, marketplaces, or other high-scale customer engagement environments.
  • Experience with at least one of the following, decisioning/online learning libraries (e.g., Vowpal Wabbit, RLlib, Stable Baselines) or large-scale ranking/recommendation tooling, causal inference tooling and experimentation platforms (A/B testing, CUPED, synthetic controls, causal forests, doubly robust methods)
  • Familiarity with behavioral science concepts (choice architecture, friction, habit formation) and designing interventions that are effective and compliant.
  • Experience with Databricks, Snowflake, or similar platforms; strong practical MLOps experience (model deployment patterns, monitoring, drift detection, retraining, and reproducibility).

Chase is a leading financial services firm, helping nearly half of America’s households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs.

We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions.  We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.

Equal Opportunity Employer/Disability/Veterans

Sponsorship evidence

Why Openbound reached the conclusions above.

Visa sponsorship evidence

Current posting

Silent on sponsorship

Other openings

132 of 7466 recent openings at this employer state a sponsorship restriction.

Employer H-1B history

3,104
recent certified H-1B filings
896
new-hire petitions
37
filings for similar roles
2,744
so far in FY2026

Filed titles like this role: vice president applied ai ml · vice president ai ml · applied ai ml - vice president · vice president - applied ai ml

More evidence details
  • 37 certified H-1B filings for this same role, 896 new-hire petitions across the employer, and 892 new hires already this year
  • 37 certified H-1B filings for this same role
  • 3,104 recent certified H-1B filings across the employer
  • Still filing this year — 2,744 filings in FY2026
  • 1 USCIS H-1B new-employment approval, counted separately from LCA filings
  • Strong filing activity in NY
  • 1 further USCIS approval for extensions or transfers
  • 132 other recent postings at this company state a sponsorship restriction
  • The posting says nothing about sponsorship either way

Strong filing activity in NY.

Green card sponsorship evidence

Employer PERM history

467
recent certified PERM filings
3
filings for similar roles
77
filings in this location
Certified PERM filings by fiscal year
20230
202459
2025711
2026740YTD

Filing history reflects past employer behavior; it isn't a promise for this opening.

All open roles at JPMorgan Chase
How Openbound evaluates sponsorship

Visa history uses official U.S. Department of Labor H-1B LCA disclosure data and USCIS H-1B petition history. Green card history uses DOL PERM disclosure data. Each is read for the employer as a whole, for roles like this one, and for this location, weighted toward the most recent fiscal years.

An employer is matched to its filing entities by verified legal name and reviewed aliases; a match is never made on a name resemblance alone. Where no verified entity can be matched, the page says so and draws no conclusion from the absence. 1,128 filing titles were examined for this employer.

What this posting states outranks history in both directions, and an employer's published policy outranks past filings. Filing history reflects past behavior; it is not a promise of sponsorship for this opening, and none of this is legal advice.