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About This Role
JOB DESCRIPTION
Join us to shape how we measure and improve the software delivery experience across Chase. You will lead a team that turns product and engineering data into clear, trusted insights that influence strategy, investment decisions, and roadmaps. This role offers the opportunity to define measurement frameworks in an area where standards are still evolving, and to build a culture of analytical rigor and practical impact. If you enjoy ambiguous problems, influencing senior stakeholders, and developing talent, you will thrive with us.
Job summary
As a Vice President, Data Science Lead in the Product, Experience and Technology Analytics team, you will define and execute the analytics strategy that measures developer productivity, technology efficiency, and product value across internal platforms and tools. You will lead data scientists and analysts to build measurement frameworks, dashboards, and models that quantify adoption, engagement, and outcomes across the software development lifecycle. You will partner closely with leaders across Product, Technology, and Finance to translate complex findings into clear narratives that drive decisions. You will set priorities, raise the bar on analytical rigor, and help your team deliver high\-impact insights at scale.
Our analytics work focuses on initiatives that improve software delivery and developer workflows, including measurement of CI/CD enhancements and adoption and impact of generative AI solutions embedded in engineering processes. You will help establish consistent definitions and reporting for metrics such as cycle time, throughput, and product adoption funnels, enabling leaders to understand what is working and where to invest next.
Job responsibilities
- Lead, manage, and develop a team of data scientists and analysts through goal setting, coaching, feedback, and performance management.
- Define and own measurement frameworks that quantify adoption, engagement, feature effectiveness, and value delivery across internal developer platforms and tools.
- Partner with senior product and engineering leaders to align analytics priorities with developer productivity and technology efficiency objectives.
- Analyze large, complex datasets (for example, pipeline events, activity logs, and usage telemetry) to identify trends and opportunities across the software development lifecycle.
- Direct the development of models that quantify the impact of workflow automation and generative AI tools on delivery speed and engineering outcomes. J
- Guide experiment design and evaluation to test hypotheses on adoption, feature changes, and workflow improvements, ensuring results are methodologically sound.
- Oversee dashboards and recurring reporting that make key metrics easy to understand and actionable for leaders and teams.
- Drive scalable analytics engineering pipelines and dependable data flows from source systems to reporting and modeling layers.
- Communicate analytical findings as clear, decision\-ready recommendations for senior stakeholders across Product, Technology, and Finance.
- Set standards for analytical rigor, documentation, and reusable assets, improving consistency across workstreams.
- Stay current on product analytics and developer productivity measurement practices, bringing new approaches into the team's work.
Required qualifications, capabilities, and skills
- Bachelor's degree in data science, statistics, computer science, or a related field.
- Six or more years of experience in data science, product analytics, or a related analytics role.
- Two or more years of people management experience, including coaching, performance management, and team development.
- Demonstrated ability to define metrics and measurement frameworks for product adoption, engagement, and value.
- Proven ability to structure ambiguous problems and deliver clear analytical approaches and outputs.
- Strong proficiency in SQL and Python for analysis, and experience with data visualization tools used for executive reporting.
- Experience working with modern data warehouse or lakehouse technologies.
- Strong foundation in machine learning, statistical modeling, and data mining techniques.
- Excellent communication and presentation skills, including ability to influence technical and non\-technical senior stakeholders.
- Demonstrated ability to manage competing priorities and deliver results in a fast\-paced environment.
Preferred qualifications, capabilities, and skills
- Master's degree or PhD in a quantitative field.
- Experience leading analytics for platform, infrastructure, or internal tooling products.
- Familiarity with Agile delivery practices and common work management tools used to run sprints and track delivery.
- Experience with analytics engineering or orchestration frameworks (for example, dbt or Airflow).
- Working knowledge of software delivery lifecycle concepts and related operational metrics.
- Familiarity with AI\-assisted development tools used to support coding and delivery workflows.
- Experience building or scaling an analytics team function, including defining standards and repeatable processes.
ABOUT US
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
ABOUT THE TEAM
Our Consumer \& Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most\-used digital solutions – all while ranking first in customer satisfaction.
The CCB Data \& Analytics team responsibly leverages data across Chase to build competitive advantages for the businesses while providing value and protection for customers. The team encompasses a variety of disciplines from data governance and strategy to reporting, data science and machine learning. We have a strong partnership with Technology, which provides cutting edge data and analytics infrastructure. The team powers Chase with insights to create the best customer and business outcomes.
Salary Context
This $142K-$210K range is above the median for Data Scientist roles in our dataset (median: $160K across 245 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,133 AI roles we're tracking, Data Scientist positions make up 8% of the market. At JPMorganChase, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 868 positions with disclosed compensation. This role's midpoint ($176K) sits 11% below the category median. Disclosed range: $142K to $210K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
JPMorganChase AI Hiring
JPMorganChase has 82 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer, MLOps Engineer. Positions span Plano, TX, US, Brooklyn, NY, US, New York, NY, US. Compensation range: $115K - $325K.
Location Context
AI roles in New York pay a median of $211,000 across 2,760 tracked positions. That's 5% above the national median.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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