Barclays

Data Scientist

Gurugram, Haryana, India

₹12 – ₹22 LPA

Posted Aug 10, 2026

Job Overview

Barclays is hiring for the role of Data Scientist based in Gurugram, Haryana, India for candidates with 0 – 2 Years of experience. Read the full details below and apply through the official link before the last date.

Company: Barclays
Role: Data Scientist
Qualification: Not specified in the official listing
Experience: 0 – 2 Years
Salary: ₹12 – ₹22 LPA
Location: Gurugram, Haryana, India

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About Barclays

Barclays is actively hiring for the position of Data Scientist. Joining a growing team like Barclays can be a great step in your career, especially for 0 – 2 Years professionals looking for new opportunities in Gurugram, Haryana, India.

Job Description

As a Data Scientist on Barclays' Decision Intelligence team, you won't just be running one-off analyses — you'll be building the predictive models and forecasting tools that inform decisions about customers, risk, and commercial strategy at bank scale. Barclays frames the mandate broadly: apply advanced analytics, statistics, and machine learning to genuinely complex business problems, using large-scale datasets that few companies outside global banking ever get to work with. Day to day, that means moving through the full data science lifecycle — pulling and cleaning data, engineering features, building and validating models, and getting them into production through CI/CD pipelines rather than leaving them as notebooks on a laptop. You'll work alongside business, technology, and risk stakeholders, so the role rewards people who can turn a messy business question into a testable analytical approach, then translate results back into something a non-technical stakeholder can act on. For anyone early in their data science career, this is a strong environment to learn in — you're exposed to the discipline of shipping models into a regulated production environment, a different (and more valuable) skill than building models purely for a portfolio project.

Key Responsibilities

Sourcing and preparing data. You'll identify, collect, and extract data from a mix of internal and external sources, then take on the less glamorous but critical work of cleaning, wrangling, and transforming it so it's genuinely fit for analysis. In banking, data-quality issues carry real downstream consequences, so this step matters more than it might elsewhere. Building and maintaining data pipelines. Rather than one-off scripts, you'll develop and maintain pipelines that automate data acquisition and processing, so models keep running on fresh, reliable data without manual intervention every time. Designing statistical and machine learning models. This is the analytical core of the role — designing and running statistical and ML models to uncover patterns, trends, and relationships hidden in the data. Developing predictive models. You'll build and implement models that forecast outcomes and flag potential risks or opportunities before they materialize, feeding directly into how the business plans ahead. Partnering with the business. You'll work closely with business stakeholders to spot where data science can genuinely move the needle, rather than waiting to be handed a fully-scoped problem.

Required Skills

Python and SQL. These are the two languages you'll live in daily — Python for building and testing models, SQL for querying the large datasets Barclays' systems hold. Close-to-production-quality code matters more here than in a typical academic setting. Machine learning development and deployment. Barclays wants people who've taken both supervised techniques (classification, regression) and unsupervised ones (clustering) from a notebook into something that actually runs in production — a different skill from simply knowing the algorithms. Statistical analysis and experimentation. A solid grounding in statistics underpins almost everything here, from validating that a model's results are real to designing a fair test or experiment. Large-scale data handling. Real comfort with messy, large, and varied datasets — cleaning them, engineering useful features, and tuning models to perform at scale, not just on a tidy sample. Cloud analytics platforms (AWS or equivalent). Barclays' data science workflows run on cloud infrastructure, so familiarity with AWS, or a comparable environment, for storage, compute, and deployment is expected. Spark/PySpark and distributed processing. Listed as advantageous rather than mandatory, but it signals you can handle datasets too large for a single machine — a real asset at banking scale. Software engineering and MLOps practices. Version control, proper testing, and structured deployment show you can work like an engineer, not just experiment in isolation — a baseline expectation now, not a bonus. Barclays also calls out softer skills: translating technical findings into business terms, the confidence to challenge stakeholders constructively using data, a commercial mindset focused on measurable impact, and real curiosity — often what separates a good data scientist from one who actually gets to influence decisions.

Eligibility Criteria

Barclays hasn't attached a specific degree or branch requirement to this listing — there's no "must hold a B.Tech in X" line anywhere in the posting. That said, the skill requirements (Python, SQL, machine learning, statistics, cloud platforms) point toward candidates from Computer Science, IT, Statistics, Data Science, Applied Mathematics, or Engineering backgrounds with solid quantitative coursework or project work. More important than the exact degree is the experience bar. This listing asks for hands-on experience deploying ML models and working with large, messy datasets — not just classroom exposure — putting it closer to early-career than a pure campus hire, even though Barclays doesn't state an exact number of years. If you're a fresher with strong personal or academic ML projects — ideally something you've actually deployed, not just trained — it's still worth applying, but be ready to speak concretely about that hands-on work.

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Salary Details

The expected salary for the Data Scientist role at Barclays is ₹12 – ₹22 LPA. Final compensation may vary based on skills, interview performance and overall experience.

Selection Process

Barclays' standard experienced-hire process runs through four broad stages. First, your application — your CV and, if you choose, a cover letter. Second, an assessment covering workplace behaviors and role-relevant ability. Third, one or more interviews focused on your past experience and technical skills — for a Data Scientist role, expect this to include a technical or case component alongside behavioral questions. Finally, a screening stage where Barclays gathers any additional information needed to complete background and eligibility checks before an offer goes out. Exact timing varies by team and applicant volume, so don't read too much into gaps between stages.

Company Culture at Barclays

Barclays structures its India offices around hybrid working, with fixed "anchor days" set by each business area rather than one company-wide policy — worth clarifying with the hiring manager if flexibility matters to you. The new Gurugram campus at DLF Downtown was purpose-built with collaborative workspaces, wellness rooms, on-site cafeterias, and fitness facilities, part of a broader push to make its India campuses long-term hubs rather than back-office space.

Barclays frames its culture around five stated values — Respect, Integrity, Service, Excellence, and Stewardship — alongside an "Empower, Challenge, Drive" mindset. For people-manager tracks, it also references a "LEAD" framework (Listen, Energise, Align, Develop). As with any large employer, how these translate into daily reality is worth probing with current or former employees if you get the chance.

Career Growth Path

Barclays doesn't publish a role-specific promotion ladder in this listing, but based on how the bank grades its Data & Analytics roles, a typical path looks roughly like this:

Analyst → Associate / Senior Data Scientist → Assistant Vice President (AVP) → Vice President (VP) → Director → Managing Director

This listing sits at Analyst grade — Barclays' entry-to-early-career banding for individual contributors, distinct from a graduate or internship program. Progression through these grades in banking typically depends on a mix of tenure, demonstrated technical depth, and, especially past the AVP level, your ability to influence stakeholders and own larger pieces of analytical strategy rather than just build models.

Things to Know Before You Apply

A few practical notes. Work location is Barclays' DLF Downtown campus in Gurugram — confirm the exact commute before applying if that matters to you. Hybrid working applies, but the specific in-office days are set by the business area, so ask directly if you're shortlisted. There's no stated application deadline, so don't wait — postings like this are often filled on a rolling basis and can close once a strong shortlist is built. Given the experience level this listing implies, brush up specifically on production ML and MLOps concepts, not just model-building — that's where this role differs from a typical academic data science interview.

Frequently Asked Questions

›What is the role offered by Barclays?

Barclays is hiring for the role of Data Scientist in Gurugram, Haryana, India.

›Who can apply for this job?

›What is the last date to apply?

Apply as soon as possible — the role may close once positions are filled.

›How do I apply for this job?

Click the "Apply for this Job" button on this page to be redirected to the official application link of Barclays.

Apply for this Job

Don't miss this opportunity at Barclays. Click below to apply through the official application link.

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