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Data Science Product Manager Jobs (NOW HIRING)

PROPRIUS

Director Data Science

San Francisco, CA

This is your chance to build a department, define a strategy, and implement a road-map to create and build data science products. The department will be part of a new innovation center, essentially a ...

CarGurus

Manager, Data Science

Boston, MA · On-site

Role overview The Manager, Data Science will lead an Inventory & Dealer Data Science team focused ... The Manager partners with Product, Engineering, Analytics, and cross-functional stakeholders to ...

Carmax

Data Science Manager, Personalization

Richmond, VA · On-site

$120K - $192K/yr

Our systems are used in dozens of product use cases across the retail and wholesale businesses ... To be clear: this is a data scientist role, not a product manager role. You'll still be hands-on ...

Hinge Health, Inc

Data Science Manager

San Francisco, CA · On-site

$220K - $330K/yr

We are seeking a Manager, Data Science to lead the data strategy for two critical areas of our ... Product Data Quality & Infrastructure: Drive the "Product Data Quality" initiative for your domains.

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$51.5K

$159.4K

$197K

How much do data science product manager jobs pay per year?

As of Jul 23, 2026, the average yearly pay for data science product manager in the United States is $159,405.00, according to ZipRecruiter salary data. Most workers in this role earn between $141,000.00 and $197,000.00 per year, depending on experience, location, and employer.

What is a Data Science Product Manager?

A Data Science Product Manager is a professional who bridges the gap between data science teams and business objectives by guiding the development of data-driven products. They work closely with data scientists, engineers, and stakeholders to define product vision, prioritize features, and ensure successful product delivery. Their role involves understanding both the technical aspects of machine learning and analytics as well as user needs and business strategy. This ensures that data-powered products are effective, user-focused, and aligned with organizational goals.

What is the hottest job of the 21st century?

Data Science Product Managers are among the most in-demand roles in the 21st century, combining skills in data analysis, product development, and strategic planning. They oversee data-driven products and require knowledge of tools like SQL, Python, and machine learning, often working in fast-paced tech environments. The role is expected to grow as organizations increasingly rely on data to make decisions.

Is 40 too late for data science?

A Data Science Product Manager can enter the field at age 40, as experience, domain knowledge, and skills like programming and statistical analysis are highly valued. Many professionals transition into data science roles later in their careers, and continuous learning through certifications or courses can facilitate this shift. Age is less important than relevant skills and experience in the data science industry.

Can data scientists make $300k?

Data science product managers and senior data scientists with extensive experience, specialized skills, and working in high-cost-of-living areas can earn salaries of $300,000 or more. Achieving this level often requires advanced knowledge of machine learning, strong business acumen, and leadership responsibilities, along with experience at top companies or in executive roles.

What are the key skills and qualifications needed to thrive as a Data Science Product Manager, and why are they important?

To thrive as a Data Science Product Manager, you need a strong background in product management, data analytics, and a foundational understanding of machine learning, often supported by a degree in a technical or quantitative field. Familiarity with tools like SQL, Python, JIRA, and knowledge of data platforms and agile methodologies is typically required. Excellent communication, strategic thinking, and the ability to bridge technical and non-technical teams are vital soft skills. These competencies ensure successful product development, effective stakeholder alignment, and the delivery of impactful data-driven solutions.

What is the difference between Data Science Product Manager vs Data Analyst?

AspectData Science Product ManagerData Analyst
Required credentialsBackground in data science, product management, or related fields; often requires experience with machine learning and data-driven product developmentTypically holds a degree in statistics, mathematics, or business; skills in data visualization and basic analytics
Work environmentCollaborates with product teams, data scientists, engineers; focuses on developing data products and strategiesWorks with business units to interpret data, generate reports, and support decision-making
Employer and industry usageUsed in tech companies, e-commerce, and organizations developing data-driven productsCommon across finance, marketing, healthcare, and business intelligence roles

The main difference is that Data Science Product Managers oversee the development of data products and strategies, requiring a blend of product management and data science skills. Data Analysts focus on interpreting data and generating insights to support business decisions. Both roles are essential in data-driven organizations but serve different functions within the data ecosystem.

How does a Data Science Product Manager typically collaborate with data scientists and engineers during a product lifecycle?

A Data Science Product Manager plays a crucial role in bridging the gap between business objectives and technical teams. Throughout the product lifecycle, they work closely with data scientists to define project goals, prioritize features, and translate business needs into actionable data-driven solutions. They also coordinate with engineers to ensure the seamless integration of machine learning models into products, address technical constraints, and facilitate communication between cross-functional teams. This collaborative approach ensures that data science initiatives are both technically feasible and aligned with overall business strategy.

Can a data scientist be a product manager?

A data scientist can transition to a product manager role, especially if they develop skills in project management, user experience, and business strategy. While the roles have different focuses—data scientists analyze data and product managers oversee product development—both require strong communication and cross-functional collaboration. Experience with tools like A/B testing, roadmapping, and stakeholder management can facilitate this transition.
More about Data Science Product Manager jobs
What cities are hiring for Data Science Product Manager jobs? Cities with the most Data Science Product Manager job openings:
What states have the most Data Science Product Manager jobs? States with the most job openings for Data Science Product Manager jobs include:
What job categories do people searching Data Science Product Manager jobs look for? The top searched job categories for Data Science Product Manager jobs are:
Data Science Product Manager jobs near you
Infographic showing various Data Science Product Manager job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 84% Full Time, 11% Part Time, 1% Temporary, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $159,405 per year, or $76.6 per hour.
Corporate Finance - Data Science Product Associate

Corporate Finance - Data Science Product Associate

JP Morgan Chase

Newark, DE • On-site

Full-time

Medical, Retirement

Posted 20 days ago

JPMorgan Chase & Co. rating

8.0

Company rating: 8.0 out of 10

Based on 492 frontline employees who took The Breakroom Quiz

60th of 150 rated banks

Job description


Join a team building modern data and analytics products that power Finance at scale. You'll partner with product, data science, and engineering to deliver usercentric capabilities that improve data quality and accelerate trusted reporting. Your work will shape modelpowered features, intuitive dashboards, and controls that leaders rely on. Grow your impact through clear ownership, mentorship, and opportunities to drive change across highvisibility programs.

Job Summary
As a Data Science Product Associate in the Firmwide Finance Business Architecture Core Services Team, you transform complex business needs into analyticsdriven features your users love. You collaborate with product managers, data scientists, and engineers to define requirements, acceptance criteria, and success metrics that tie directly to outcomes. You support development and testing of artificial intelligence and machine learning models and the controls that safeguard their use. You build executiveready dashboards and narratives that inform decisions and prioritize the roadmap. You communicate clearly and inclusively, turning analytics into action for Finance controllers and reporting teams.


You will help standardize reporting and playbooks that scale insights delivery across Finance, Treasury and the Chief Investment Office, and Wholesale Credit Risk platforms. You will strengthen data validation, lineage, and documentation, aligning to privacy, security, and model risk standards. You will facilitate crossfunctional forums, synthesize feedback, and ensure analytics and controls are deployed reliably in strategic and legacy environments. Your work enables faster, more reliable close and reporting cycles while improving transparency and governance.

Job Responsibilities

  • Translate business problems into analytical requirements and clear acceptance criteria; refine epics and write user stories that maximize value.
  • Analyze product usage, customer behavior, and model performance to surface insights that inform prioritization and roadmap decisions.
  • Build executiveready dashboards and narratives; design A/B tests and pilots, define success metrics, and evaluate outcomes including return on investment.
  • Partner with engineering on data validation, lineage, documentation, and control alignment; ensure compliance with privacy, security, and model risk requirements.
  • Maintain and prioritize a backlog of data enhancements aligned to business outcomes; manage delivery using Agile practices and tooling.
  • Facilitate crossfunctional forums; synthesize feedback into clear recommendations and communicate complex findings in business language.
  • Standardize reporting, create playbooks, and streamline processes for repeatable, scalable insights delivery.
  • Support development and testing of AI and machine learning models and data controls to improve data quality and operational efficiency.

Required Qualifications, Capabilities, and Skills

  • Bachelor's degree in a quantitative field (for example, computer science, statistics) and a minimum of four years in product analytics, business analytics, or data science within a digital or product environment.
  • Proficiency in SQL and a data visualization tool; familiarity with cloud data platforms; handson experience with Amazon Web Services and Databricks.
  • Proficiency in Python or R for exploratory analysis and model evaluation; experience with time series analysis and modeling, and training or finetuning machine learning models.
  • Experience with experimentation (A/B testing), cohort analysis, key performance indicators (KPIs), and measurement plans for modelpowered features.
  • Ability to manage multiple workstreams under tight deadlines; strong analytical, problemsolving, and collaboration skills to influence decisions across business and technology.
  • Indepth knowledge of data and business intelligence concepts, including extract, transform, load (ETL), data modeling, and reporting automation.
  • Strong storytelling skills with the ability to craft clear, concise narratives from complex data for executive and nontechnical audiences.

Preferred Qualifications, Capabilities, and Skills

  • Experience with Agile delivery methodologies and tools to manage both technical and functional work.
  • Exposure to machine learning productization, including model monitoring, drift detection, and feature performance measurement.
  • Knowledge of banking products such as loans, deposits, cash management, derivatives, and securities from both technical and business perspectives.
  • Awareness of user interface and user experience (UI/UX) principles; experience improving interaction by integrating user needs with technical functionality.
  • Experience with Jira and Confluence.
  • Familiarity with model risk governance and documentation standards.

***Relocation assistance is not available for this role.

JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.

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.

JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans

Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we're setting our businesses, clients, customers and employees up for success.

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