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Data Analytics Associate

Pay
Pay not listed
Work setting
Not stated
Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting

What you’ll work on

Full posting
  • Perform financial analysis: variance, profitability, margin and KPI analysis

  • Design and automate management reports, dashboards and scorecards

  • Drive budgeting, re-forecast and multi-year planning cycles with Finance

  • Run EDA and statistical modelling to find drivers of financial performance

  • Build BI visualizations and narratives for senior leadership in Tableau, Power BI, Alteryx

From the employer’s posting
Perform in-depth financial analysis including revenue, expense, margin, variance, profitability, product/client profitability and KPI analysis
Design, develop and automate management reporting, dashboards and scorecards covering P&L, balance sheet, cash flow, forecasts, budgets, headcount and performance metrics
Drive the annual budgeting, mid-year re-forecast, rolling forecast and multi-year strategic planning cycles in partnership with Business Finance teams
Implement exploratory data analysis (EDA) and statistical modelling to identify trends, patterns and drivers of financial performance
Build compelling visualizations and storytelling using BI tools (Tableau, Power BI, Alteryx)
Education & alternatives
Preferred qualifications, capabilities and skills - Bachelor's Degree in Finance, Accounting, Economics, Statistics, Data Science, Computer Science or Engineering. A different field of study with significant professional experience in Financial Analytics / BI / Data Science / AI is acceptable. MBA (Finance) is a strong plus. - Exposure to Asset & Wealth Management / banking products and metrics (AUM, net flows, revenue yield, client profitability, fee waivers) is strongly preferred.
Apply at JPMorgan Chase

Posting record

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Actively hiringFirst seen by us 12 days ago, still listed, and not a repost of an earlier posting.

Status in our records
Live
First seen by us
Oct 30, 202612 days ago
Recorded sightings
13
Last seen by us
Nov 11, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 12

Similar jobs usually come down between day 5 and day 43.

At JPMorgan Chase, jobs usually come down by about day 15.

From 13,640 similar postings that came down, and 967 of JPMorgan Chase’s. Coming down is not always a hire.

JPMorgan Chase is hiring

366 open jobs

Open jobs by the week we first saw them, last 12 weeks.

  • Hiring at a steady pace
  • Takes jobs down at a usual pace
  • 3% of open jobs were reposted
Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

  • Python
  • SQL
  • Databricks
  • Excel
  • Power BI
  • Snowflake
  • Tableau
  • Keras
  • NumPy
  • pandas
  • PySpark
  • scikit-learn
Where the posting names them
- Employ scaling and automation to data preparation, consolidation and reporting workflows to improve efficiency and accuracy of recurring financial deliverables. Transform model outputs and analytical results into formats digestible to end users through visualizations, clear tables and thoughtful presentations; assist technical and non-technical stakeholders in interpreting the analysis. - Leverage AI assistants and copilots (e.g., Microsoft 365 Copilot, GitHub Copilot, and in-house Gen AI tooling) to accelerate analytics workflows — code generation, SQL/Python drafting, document summarization, and natural-language querying of financial datasets — while applying firm-approved controls on data confidentiality and model output review. Required qualifications, capabilities and skills
- Mandatory experience in Exploratory Data Analysis (EDA), regression and statistical/machine learning modelling applied to financial or business datasets. Hands-on experience with pandas and PySpark/PySQL stack. Experience in building data extraction, transformation and modelling pipelines. - Strong proficiency in SQL (Snowflake, Databricks) and Python (Pandas, NumPy, Scikit-learn, Keras, PySpark/PySQL) for data wrangling, modelling, AI pipelines and automation of financial workflows. Hands-on experience using AI skills / copilots to support analytics work — e.g., Microsoft 365 Copilot (Excel, Power BI, Word, PowerPoint), GitHub Copilot, for SQL generation, data summarization and productivity gains in day-to-day financial analytics deliverables; awareness of responsible-AI, data-privacy and model-risk guardrails. - Working proficiency in data visualization tools; strong experience with BI technologies including but not limited to Tableau, Power BI and Alteryx. Experience in end-to-end implementation of Business Intelligence (BI) reports and dashboards for finance, risk or business performance reporting. Should be good at mathematics, inferential statistics and quantitative analysis as applied to financial data and model building.
- Implement exploratory data analysis (EDA) and statistical modelling to identify trends, patterns and drivers of financial performance. Identify key financial and operational data sources that can drive business impact; ensure data quality, reconciliation and audit trails across reporting outputs. - Design and develop data mining and reporting queries from large data sets (both structured and unstructured) and convert raw financial data into clear information. Build compelling visualizations and storytelling using BI tools (Tableau, Power BI, Alteryx) that translate financial results and model outputs into clear business narratives for senior leadership. - Employ scaling and automation to data preparation, consolidation and reporting workflows to improve efficiency and accuracy of recurring financial deliverables. Transform model outputs and analytical results into formats digestible to end users through visualizations, clear tables and thoughtful presentations; assist technical and non-technical stakeholders in interpreting the analysis.
- Strong proficiency in SQL (Snowflake, Databricks) and Python (Pandas, NumPy, Scikit-learn, Keras, PySpark/PySQL) for data wrangling, modelling, AI pipelines and automation of financial workflows. Hands-on experience using AI skills / copilots to support analytics work — e.g., Microsoft 365 Copilot (Excel, Power BI, Word, PowerPoint), GitHub Copilot, for SQL generation, data summarization and productivity gains in day-to-day financial analytics deliverables; awareness of responsible-AI, data-privacy and model-risk guardrails. - Working proficiency in data visualization tools; strong experience with BI technologies including but not limited to Tableau, Power BI and Alteryx. Experience in end-to-end implementation of Business Intelligence (BI) reports and dashboards for finance, risk or business performance reporting. Should be good at mathematics, inferential statistics and quantitative analysis as applied to financial data and model building. - Provides explanations and information to others on topics within area of expertise; strong business partnering skills with Finance and Business stakeholders.
- Working knowledge of profitability and performance metrics: unit economics, gross/operating margin, contribution margin, EBITDA, ROE/ROA, cost-to-income ratio, NIM, fee revenue and AUM-based metrics. - Mandatory experience in Exploratory Data Analysis (EDA), regression and statistical/machine learning modelling applied to financial or business datasets. Hands-on experience with pandas and PySpark/PySQL stack. Experience in building data extraction, transformation and modelling pipelines. - Strong proficiency in SQL (Snowflake, Databricks) and Python (Pandas, NumPy, Scikit-learn, Keras, PySpark/PySQL) for data wrangling, modelling, AI pipelines and automation of financial workflows. Hands-on experience using AI skills / copilots to support analytics work — e.g., Microsoft 365 Copilot (Excel, Power BI, Word, PowerPoint), GitHub Copilot, for SQL generation, data summarization and productivity gains in day-to-day financial analytics deliverables; awareness of responsible-AI, data-privacy and model-risk guardrails.

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About JPMorgan Chase

People
About 318,512 in 2025.Annual report (SEC Form 10-K)
Company
Listed as JPM.
Layoff notices
Notices this year and last cover 152 people. The latest was filed March 23, 2026.State WARN notices

Demand for Analyst roles

New Analyst postings at the employers we follow are up 24% over the last 6 weeks compared with the 6 before.

Weekly new Analyst postings at the same employers, from the week of August 17 to the week of November 2. The fewest in a week was 966, the most 1,494, and the latest week had 1,494.
August 17Last week

Each bar is one week of new postings. The darkest bars are the last 6 weeks.

Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.

About the role

We file this job under Analyst, by the work the posting describes.

Analysts answer business questions with data. They pull numbers with SQL, build reports and dashboards, and explain what changed and why to the people making decisions.

Most postings ask for SQL, spreadsheets, a dashboard tool such as Tableau or Power BI, and clear writing.

Unlike a data scientist, an analyst mostly explains what happened rather than building models that predict what will.

Job description

View original posting ↗

You are a strategic thinker passionate about driving solutions in Financial Analytics, Reporting, Business Intelligence, Data Science (EDA and Modelling) and AI / Gen AI. You have found the right team.

As a Financial Analytics Associate within our Asset and Wealth Management Transformation and Analytics Team, you will define, refine, and deliver strategic goals for our firm by translating complex financial and operational data into actionable insights, predictive models, and AI-promoten solutions that enhance decision-making and promote business performance.

Job Responsibilities:

  • Scope, prioritize and co-ordinate activities with product owners and finance stakeholders. Partner with Finance, Risk and Business teams to identify strategic analytical, data science and AI solutions. Perform in-depth financial analysis including revenue, expense, margin, variance, profitability, product/client profitability and KPI analysis to support business decisions.
  • Design, develop and automate management reporting, dashboards and scorecards covering P&L, balance sheet, cash flow, forecasts, budgets, headcount and performance metrics. Drive the annual budgeting, mid-year re-forecast, rolling forecast and multi-year strategic planning cycles in partnership with Business Finance teams.
  • Implement exploratory data analysis (EDA) and statistical modelling to identify trends, patterns and drivers of financial performance. Identify key financial and operational data sources that can drive business impact; ensure data quality, reconciliation and audit trails across reporting outputs.
  • Design and develop data mining and reporting queries from large data sets (both structured and unstructured) and convert raw financial data into clear information. Build compelling visualizations and storytelling using BI tools (Tableau, Power BI, Alteryx) that translate financial results and model outputs into clear business narratives for senior leadership.
  • Employ scaling and automation to data preparation, consolidation and reporting workflows to improve efficiency and accuracy of recurring financial deliverables. Transform model outputs and analytical results into formats digestible to end users through visualizations, clear tables and thoughtful presentations; assist technical and non-technical stakeholders in interpreting the analysis.
  • Leverage AI assistants and copilots (e.g., Microsoft 365 Copilot, GitHub Copilot, and in-house Gen AI tooling) to accelerate analytics workflows — code generation, SQL/Python drafting, document summarization, and natural-language querying of financial datasets — while applying firm-approved controls on data confidentiality and model output review.

Required qualifications, capabilities and skills

  • Self-starter, attention to detail and results oriented, able to work under minimal guidance in a fast-paced environment.
  • Uses pertinent financial data and facts to identify and solve a range of business problems within area of expertise.
  • Mandatory experience in financial analytics, FP&A, management reporting, variance analysis and KPI/performance measurement. Strong understanding of accounting principles (US GAAP / IFRS), financial statements (P&L, balance sheet, cash flow), journal entries, intercompany, allocations, accruals and month-end close processes.
  • Working knowledge of profitability and performance metrics: unit economics, gross/operating margin, contribution margin, EBITDA, ROE/ROA,  cost-to-income ratio, NIM, fee revenue and AUM-based metrics. 
  • Mandatory experience in Exploratory Data Analysis (EDA), regression and statistical/machine learning modelling applied to financial or business datasets. Hands-on experience with pandas and PySpark/PySQL stack. Experience in building data extraction, transformation and modelling pipelines.
  • Strong proficiency in SQL (Snowflake, Databricks) and Python (Pandas, NumPy, Scikit-learn, Keras, PySpark/PySQL) for data wrangling, modelling, AI pipelines and automation of financial workflows. Hands-on experience using AI skills / copilots to support analytics work — e.g., Microsoft 365 Copilot (Excel, Power BI, Word, PowerPoint), GitHub Copilot, for  SQL generation, data summarization and productivity gains in day-to-day financial analytics deliverables; awareness of responsible-AI, data-privacy and model-risk guardrails.
  • Working proficiency in data visualization tools; strong experience with BI technologies including but not limited to Tableau, Power BI and Alteryx. Experience in end-to-end implementation of Business Intelligence (BI) reports and dashboards for finance, risk or business performance reporting. Should be good at mathematics, inferential statistics and quantitative analysis as applied to financial data and model building.
  • Provides explanations and information to others on topics within area of expertise; strong business partnering skills with Finance and Business stakeholders.
  • Experience Required: 5+ years in data/analytics related projects with 2+ years in financial analytics
  • Good communication and analytical skills, with the ability to present financial insights, model results and AI-driven outputs clearly to senior stakeholders.

Preferred qualifications, capabilities and skills

  • Bachelor's Degree in Finance, Accounting, Economics, Statistics, Data Science, Computer Science or Engineering. A different field of study with significant professional experience in Financial Analytics / BI / Data Science / AI is acceptable. MBA (Finance) is a strong plus.
  • Exposure to Asset & Wealth Management / banking products and metrics (AUM, net flows, revenue yield, client profitability, fee waivers) is strongly preferred.
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Mumbai, Maharashtra, India

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