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About This Role
JOB DESCRIPTION
Join an intellectually diverse team of economists, statisticians, engineers, and other analytics professionals focused on quantitative modeling within Community \& Consumer Banking (CCB) at JPMorganChase \& Co.
As a Data Scientist Lead, within the the Finance Decision Optimization group, you will build and deploy data\-driven solutions, collaborate with stakeholders and cross\-functional teams to define data and model requirements, design and build data pipelines, and develop complex predictive and optimization routines.
Job responsibilities:
- Build, compile, and automate scalable data pipelines, complex predictive models, and optimization routines using big data technologies (Spark, Databricks, Snowflake) on cloud platforms; transform massive volumes of data into actionable business insights and package solutions into repeatable, executable workflows for QA testing and production deployment.
- Lead solution backtesting exercises across key stakeholder domains (e.g., Fair Lending), validate model performance against historical data, identify analytical gaps and proactively surface critical issues to business and technology partners to ensure models are robust, reliable, and decision\-ready.
- Stay ahead of industry trends in data science, ML, and cloud engineering; provide informed recommendations for adopting new and emerging technologies; actively support ongoing technology evaluation processes and contribute to early\-stage proof of concept projects that test and validate innovative approaches.
- Collaborate effectively across engineering, data science, business, and external stakeholder teams; manage project delivery within timelines; ensure solutions meet critical business needs while proactively raising risks, dependencies, and blockers to the right partners before they escalate. and serve as a mentor and knowledge resource for junior staff; establish best practices in data engineering, ML modeling, and analytical automation; foster a culture of continuous learning, technical excellence, and shared ownership across the team.
- Architect and build foundational agentic workflows from the ground up — including tool/function calling, multi\-step reasoning chains, and agent orchestration patterns — while establishing early technical standards that will scale from PoC to production\-ready systems.
- Define success metrics specific to agent performance (task completion, tool\-use accuracy, reasoning consistency, failure modes); build evaluation harnesses early in the PoC stage to validate agent behavior, surface edge cases, and establish quality baselines before scaling.
- Design and prototype retrieval layers (RAG, tool\-augmented memory, knowledge base integrations) that agents rely on to take actions; ensure data quality and access controls are considered from day one of the PoC to avoid rearchitecting later and identify and mitigate risks unique to autonomous agents (unintended actions, prompt injection, cascading tool\-call failures, data leakage) and establish guardrails and human\-in\-the\-loop checkpoints early in the PoC to build a safe and auditable agent framework.
Required qualifications, capabilities and skills:
- A minimum of 5 years of relevant professional experience as a software engineer, data/ML engineer, data scientist, or AI/ML systems engineer, with a demonstrated track record of delivering complex, end\-to\-end technical solutions in production or near\-production environments; Bachelor's degree in Computer Science, Financial Engineering, MIS, Mathematics, Statistics, or another quantitative field.
- Practical knowledge of the banking sector, specifically in areas of retail deposits, auto, card, and mortgage lending, with an understanding of relevant compliance and regulatory contexts (e.g., Fair Lending).
- Working knowledge of LLMs, agentic AI frameworks, and emerging AI engineering practices, including tool/function calling, RAG architectures, prompt design, and agent orchestration patterns; eagerness to stay current with the latest advancements in Agentic AI and machine learning.
- Exceptional analytical and problem\-solving abilities with a clear understanding of business requirements; capable of translating complex technical concepts to a wide range of audiences including non\-technical stakeholders.
- Highly detail\-oriented with a proven track record of delivering tasks on schedule; able to manage multiple priorities efficiently in a fast\-paced environment while maintaining quality and meeting critical business needs.
- Excellent team player with strong interpersonal skills; able to work cross\-functionally using a consultative approach, mentor junior staff, and contribute to a culture of shared technical ownership and continuous improvement.
- Instrument agent workflows with observability (action traces, decision logs, cost and latency tracking) from the earliest prototype and synthesize PoC findings into architectural decisions, runbooks, and optimization strategies (caching, model routing, token budgets) that accelerate the path to production deployment.
Preferred qualifications, skills and capabilities:
- Proficiency in Python programming with a strong grasp of object\-oriented and functional programming concepts; experience applying Python in data processing, ML model development, and AI/LLM application development including prompt engineering and agentic workflow orchestration and hands\-on experience with LLM orchestration frameworks (e.g., LangChain, LangGraph, LlamaIndex, or similar); familiarity with embedding models, vector databases (e.g., FAISS, Pinecone, pgvector), retrieval\-augmented generation (RAG) pipelines, and evaluation frameworks for agentic systems.
- Extensive knowledge of Apache Spark with experience optimizing Spark jobs for performance and scalability within Databricks; hands\-on experience with cloud platforms (AWS EC2, EMR, S3/EFS or equivalent) and proficiency with Snowflake for large\-scale data processing and analytics.
- Advanced SQL skills for complex query writing, data manipulation, and analysis; strong experience in data engineering including ETL/ELT processes, data modeling, data governance, and compliance standards relevant to handling sensitive and regulated data and proficiency with the Python data science ecosystem (Pandas, NumPy, SciPy) and practical experience implementing and validating machine learning algorithms (e.g., XGBoost, TensorFlow) and ability to perform data analysis, cleansing, modeling (including time series and NLP), and visualization using tools such as Tableau or Alteryx to develop and automate actionable business insights.
- Expertise in Linux bash shell environment and Git for version control and collaborative development; familiarity with containerization and orchestration technologies (e.g., Docker, Kubernetes) to support scalable deployment of data and AI services and familiarity with implementing guardrails, input/output validation, human\-in\-the\-loop checkpoints, and monitoring/observability patterns (action traces, decision logs, cost and latency tracking) for AI/agentic systems operating in regulated environments.
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.
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 3,824 AI roles we're tracking, Data Scientist positions make up 7% 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 $200,000 based on 697 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
JPMorganChase AI Hiring
JPMorganChase has 68 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Engineering Manager, AI Product Manager. Positions span Jersey City, NJ, US, Tampa, FL, US, New York, NY, US. Compensation range: $130K - $325K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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