Data Science Product Senior Associate

JPMorgan Chase & Co.•Newark, DE

About The Position

Build modern data and analytics products that power Finance at scale, alongside teammates who care about craft, outcomes, and users. You will partner with product, data science, and engineering to deliver user‑centric capabilities that improve data quality and accelerate trusted reporting. Your work will shape model‑powered features, intuitive dashboards, and strong controls that leaders rely on to make decisions. Grow your impact through clear ownership, mentorship, and opportunities to drive change across high‑visibility programs. As a Data Science Product Associate in the Firmwide Finance Business Architecture Core Services Team, you transform complex business needs into analytics‑driven 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 executive‑ready 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 cross‑functional 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.

Requirements

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; hands‑on 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 fine‑tuning machine learning models.
Experience with experimentation (A/B testing), cohort analysis, key performance indicators (KPIs), and measurement plans for model‑powered features.
Ability to manage multiple workstreams under tight deadlines; strong analytical, problem‑solving, and collaboration skills to influence decisions across business and technology.
In‑depth 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 non‑technical audiences.

Nice To Haves

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.

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 executive‑ready 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 cross‑functional 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 artificial intelligence and machine learning models and data controls to improve data quality and operational efficiency.