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Data Scientist

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
  • Develop advanced SQL queries and data models for portfolio analysis, client segmentation and reporting

  • Apply data discovery to find critical data assets and map them to wealth management processes

  • Execute data classification frameworks for sensitive and confidential information

  • Create predictive and descriptive models for investment decisions, client retention and cross-sell

  • Mentor junior data practitioners on SQL modeling and privacy-aware analytics

From the employer’s posting
Develop advanced SQL queries and data models that optimize portfolio analysis client segmentation and performance reporting
Apply data discovery techniques to identify critical data assets across structured and unstructured sources and map them to key wealth management business processes
Execute data classification frameworks that categorize sensitive and confidential information
Create predictive and descriptive models that enhance investment decisions client retention and cross sell opportunities
Mentor junior data practitioners on best practices in SQL modeling privacy aware analytics and domain nuances

Tools in this posting

  • SQL
Source — Tool mentions in context
This hybrid day shift position requires deep expertise in SQL data discovery and classification with strong domain experience in asset and wealth management to drive compliant insights and strategic decision making across the enterprise.
Required Skills : Technical Skills: SQL Data Privacy Discovery Classification Data Discovery Domain Skills:Asset & Wealth Management 5.
Responsibilities : -Design and implement scalable analytics solutions that harness complex asset and wealth management datasets to deliver clear business insights while maintaining rigorous data privacy safeguards -Develop advanced SQL queries and data models that optimize portfolio analysis client segmentation and performance reporting for multiple asset classes in a hybrid work setup -Apply data discovery techniques to identify critical data assets across structured and unstructured sources and map them to key wealth management business processes -Execute data classification frameworks that categorize sensitive and confidential information to support compliant analytics and responsible data usage across global teams -Collaborate with portfolio managers risk specialists and operations teams to translate investment and client service questions into robust data science solutions with measurable outcomes -Create predictive and descriptive models that enhance investment decisions client retention and cross sell opportunities while embedding privacy by design principles into each solution -Establish repeatable data quality checks and validation routines that strengthen the reliability of risk metrics performance indicators and regulatory reports used in asset and wealth management -Document data lineage usage patterns and privacy controls that clarify how information flows through analytical pipelines and support audit readiness for internal and external reviews -Partner with information security and compliance experts to align data discovery and classification practices with evolving regulations and firm policies ensuring resilient protection of client information -Guide the integration of new data sources into analytic platforms by assessing data sensitivity access needs and fit for purpose within wealth management strategies -Mentor junior data practitioners on best practices in SQL modeling privacy aware analytics and domain nuances so that team output remains consistent and high quality -Communicate findings through clear narratives and visual summaries that help business stakeholders understand drivers of performance and the privacy implications of proposed actions -Continuously refine analytic approaches by monitoring model performance feedback and changes in market conditions to keep insights relevant and aligned with organizational objectives and societal trust in financial services -Qualifications -Demonstrate extensive hands on experience using SQL to design complex queries data transformations and performance optimized models within large scale financial data environments -Exhibit deep familiarity with data privacy principles regulations and control mechanisms that govern the handling of client and transaction information in asset and wealth management contexts -Show strong capability in data discovery methods and tools enabling accurate identification of critical data elements and sources required for investment analytics and regulatory reporting -Apply structured data classification methodologies that distinguish sensitive confidential and operational data in support of secure analytics and compliant information sharing -Bring solid domain expertise in asset and wealth management including understanding of portfolios investment products client profiling risk measures and associated reporting practices -Display experience working in hybrid teams using collaborative platforms and version control practices to manage analytic assets and documentation across geographies -Demonstrate ability to design and deploy statistical or machine learning models that create tangible improvements in investment insights operational efficiency or client engagement metrics -Illustrate strong problem solving skills and communication abilities to engage with nontechnical stakeholders clarify complex findings and influence decisions with data driven arguments -Show a track record of adhering to organizational governance frameworks data standards and quality benchmarks that ensure analytic outputs remain accurate trusted and reusable 9.

Posting record

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Actively hiringFirst seen by us 5 days ago, still listed, and not a repost of an earlier posting.

Status in our records
Live
First seen by us
Nov 4, 20265 days ago
Recorded sightings
6
Last seen by us
Nov 9, 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 5

Early. Similar jobs usually come down between day 7 and day 57.

At Cognizant, jobs usually come down by about day 21.

From 10,789 similar postings that came down, and 395 of Cognizant’s. Coming down is not always a hire.

Cognizant is hiring

139 open jobs

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

  • Hiring at a steady pace
  • Takes jobs down quickly
  • 5% of open jobs were reposted
Hiring record

Your next step

Check the employer’s posting for the current role and application details.

Already applied? Track this application

Has Cognizant posted this job before?

Yes. We’ve followed 3 other Data Scientist jobs there since August 2026. All 3 are still up.

  1. BengaluruStill up after 90 days, first seen Aug 11
  2. BengaluruStill up after 38 days, first seen Oct 2
  3. ChennaiStill up after 7 days, first seen Nov 3
  4. This jobUp 5 days so far, first seen Nov 4

Large employers often post one title for several teams, so these can be separate openings.

About Cognizant

People
About 351,600 in 2025, up 4% from the year before.Annual report (SEC Form 10-K)
Company
Listed as CTSH.
H-1B visas
1,401 approved for new hires in fiscal 2025.US Citizenship and Immigration Services

Demand for Data Scientist roles

New Data Scientist postings at the employers we follow are down 5% over the last 6 weeks compared with the 6 before.

Weekly new Data Scientist postings at the same employers, from the week of August 17 to the week of November 2. The fewest in a week was 1,408, the most 2,964, and the latest week had 2,139.
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 Data Scientist, by the work the posting describes.

Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.

Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.

Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.

US government figures for data scientists

Median yearly pay, May 2025
$120,230
Expected change, 2025 to 2035
34.6% more jobsagainst 3.5% for all jobs

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.

Job description

View original posting ↗


1. Job Title : Data Scientist 2. Job Summary : Data Scientist role in a global asset and wealth management organization focused on designing and executing advanced analytics solutions that safeguard data privacy while improving client outcomes and operational efficiency. This hybrid day shift position requires deep expertise in SQL data discovery and classification with strong domain experience in asset and wealth management to drive compliant insights and strategic decision making across the enterprise. 3. Experience : 8 - 12 years 4. Required Skills : Technical Skills: SQL Data Privacy Discovery Classification Data Discovery Domain Skills:Asset & Wealth Management 5. Nice to have skills : Domain Skills: 6. Technology : Custom Service 7. Shift : Day 8. Responsibilities : -Design and implement scalable analytics solutions that harness complex asset and wealth management datasets to deliver clear business insights while maintaining rigorous data privacy safeguards -Develop advanced SQL queries and data models that optimize portfolio analysis client segmentation and performance reporting for multiple asset classes in a hybrid work setup -Apply data discovery techniques to identify critical data assets across structured and unstructured sources and map them to key wealth management business processes -Execute data classification frameworks that categorize sensitive and confidential information to support compliant analytics and responsible data usage across global teams -Collaborate with portfolio managers risk specialists and operations teams to translate investment and client service questions into robust data science solutions with measurable outcomes -Create predictive and descriptive models that enhance investment decisions client retention and cross sell opportunities while embedding privacy by design principles into each solution -Establish repeatable data quality checks and validation routines that strengthen the reliability of risk metrics performance indicators and regulatory reports used in asset and wealth management -Document data lineage usage patterns and privacy controls that clarify how information flows through analytical pipelines and support audit readiness for internal and external reviews -Partner with information security and compliance experts to align data discovery and classification practices with evolving regulations and firm policies ensuring resilient protection of client information -Guide the integration of new data sources into analytic platforms by assessing data sensitivity access needs and fit for purpose within wealth management strategies -Mentor junior data practitioners on best practices in SQL modeling privacy aware analytics and domain nuances so that team output remains consistent and high quality -Communicate findings through clear narratives and visual summaries that help business stakeholders understand drivers of performance and the privacy implications of proposed actions -Continuously refine analytic approaches by monitoring model performance feedback and changes in market conditions to keep insights relevant and aligned with organizational objectives and societal trust in financial services -Qualifications -Demonstrate extensive hands on experience using SQL to design complex queries data transformations and performance optimized models within large scale financial data environments -Exhibit deep familiarity with data privacy principles regulations and control mechanisms that govern the handling of client and transaction information in asset and wealth management contexts -Show strong capability in data discovery methods and tools enabling accurate identification of critical data elements and sources required for investment analytics and regulatory reporting -Apply structured data classification methodologies that distinguish sensitive confidential and operational data in support of secure analytics and compliant information sharing -Bring solid domain expertise in asset and wealth management including understanding of portfolios investment products client profiling risk measures and associated reporting practices -Display experience working in hybrid teams using collaborative platforms and version control practices to manage analytic assets and documentation across geographies -Demonstrate ability to design and deploy statistical or machine learning models that create tangible improvements in investment insights operational efficiency or client engagement metrics -Illustrate strong problem solving skills and communication abilities to engage with nontechnical stakeholders clarify complex findings and influence decisions with data driven arguments -Show a track record of adhering to organizational governance frameworks data standards and quality benchmarks that ensure analytic outputs remain accurate trusted and reusable 9. Job Location : Primary Location :INTSHYDA02(ITIND Madhapur Bld 12A (MDH) SEZ) Alternate Location :INWBKOLA01(ITIND Technocomplex (TCX) STPI) Alternate Location 1 :NA NA 10. Job Type : Sr. Associate - Projects [60PM00] 11. Demand Requires Travel? : No 12. Certifications Required : NA

Source details

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Location & working pattern

Hyderabad, Telangana, India

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