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REQUIREMENT_ID_1989 • 3-DAY_ACTIVE_POLICY JD

AI/ML Data Scientist

Company
Company Barclays
Type
Opportunity Type Internship
Salary
Stipend / Salary ≈ 12 LPA
Location
Location Gurugram, India
Posted Date
Posted Date Yesterday
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Python SQL Statistics Machine Learning Data Visualization Feature Engineering Model Evaluation Problem Solving Communication
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Aptitude & Reasoning Practice Open Resource ↗
Helps you sharpen quantitative and logical reasoning skills essential for the online assessment stage.
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Barclays Recruitment Process Insights Open Resource ↗
Provides detailed experiences of candidates who have cleared Barclays interviews, useful for preparation strategy.
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Data Scientist Interview Preparation Guide Open Resource ↗
Covers common data‑science interview topics, sample questions and tips tailored for Barclays roles.
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Algorithm & Data Structure Problems Open Resource ↗
Offers a wide range of coding problems to practice Python implementation and problem‑solving speed.

• Minimum qualification: B.Tech/B.E, B.Sc, M.Tech, M.Sc or equivalent in Computer Science, Data Science, Statistics, Mathematics, Electronics & Communication or related fields. • Minimum aggregate: 60% (or CGPA 6.0/10). • No active backlogs at the time of joining. • Batch year: 2024 or 2025 (freshers). • Strong foundation in programming, statistics and machine‑learning concepts. • Good communication skills in English.

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Round 1: Online Assessment (aptitude, logical reasoning and basic programming)
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Round 2: Technical Interview (ML concepts, Python coding, case studies)
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Round 3: HR Interview (fitment, motivation and cultural alignment)
Barclays is a leading global financial services group with a heritage spanning over three centuries. Headquartered in London, the bank operates in more than 40 countries and serves millions of customers worldwide. In India, Barclays has a strong presence in major financial hubs such as Gurugram, Mumbai and Bengaluru, offering a wide range of services from investment banking to wealth management. The firm is investing heavily in technology and innovation, positioning itself as a digital‑first organization that leverages data, artificial intelligence and machine‑learning to deliver smarter financial solutions. The AI/ML Data Scientist role is designed for fresh graduates who are passionate about turning raw data into actionable insights. As part of the Data & Analytics team, you will collaborate with product managers, engineers and business stakeholders to build predictive models, develop data pipelines and create visual dashboards that drive strategic decisions across the bank’s retail and corporate divisions. This is an excellent entry point for candidates eager to work on real‑world problems in a highly regulated, data‑rich environment. Key responsibilities include: 1. Collecting, cleaning and preprocessing large structured and unstructured datasets from internal and external sources. 2. Conducting exploratory data analysis to uncover patterns, trends and anomalies. 3. Designing, training and validating machine‑learning models for use‑cases such as credit risk scoring, fraud detection and customer segmentation. 4. Implementing feature engineering techniques to improve model performance and robustness. 5. Deploying models into production using cloud platforms and monitoring their performance over time. 6. Creating interactive visualizations and dashboards using tools like Tableau or PowerBI to communicate findings to non‑technical stakeholders. 7. Writing clear documentation and presenting results in concise business‑focused reports. 8. Staying up‑to‑date with the latest AI/ML research and recommending innovative solutions that align with Barclays’ digital strategy. 9. Collaborating with data engineers to ensure data pipelines are reliable, scalable and secure. 10. Participating in code reviews and adhering to best practices for reproducible research and model governance. The technical stack for this role typically includes Python (Pandas, NumPy, Scikit‑learn), SQL, cloud services (AWS/GCP), containerisation (Docker), and deep‑learning frameworks such as TensorFlow or PyTorch. Knowledge of statistical modelling, time‑series analysis and data‑visualisation libraries (Matplotlib, Seaborn) is also valuable. Barclays offers a clear growth path for data scientists: junior analysts progress to senior data scientists, then to lead data scientists or AI product managers, with opportunities to move across business lines or into global roles. The bank encourages continuous learning through internal training programmes, certifications and mentorship, ensuring that fresh talent can quickly acquire the expertise needed for advanced AI projects. Why join Barclays? You will be part of a globally recognised brand that values innovation, diversity and ethical banking. The company provides a collaborative culture, competitive compensation, and exposure to cutting‑edge financial technology. Freshers get the chance to work on high‑impact projects from day one, gaining experience that is highly valued across the fintech ecosystem.

Barclays — Data Analytics & SQL Interview Guide

Previously asked questions, exam syllabus, coding benchmarks & round strategy.

APTITUDE & LOGIC 📖
Aptitude Practice Questions & Online Mock Tests

Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test.

Open Resource ↗
COMPANY GUIDE 🎯
Barclays Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for Barclays and off-campus tech roles.

Open Resource ↗
MOCK TESTS & PAPERS 📝
Comprehensive Interview Prep Resources & Syllabus

Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds.

Open Resource ↗
CODING PRACTICE 💻
Algorithm and Data Structure Problem Set

Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.

Open Resource ↗
Round 1: Online Assessment (OA)
Aptitude, Quantitative Logic & 2 Coding Problems
Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1
Core Tech Stack (Python, SQL, Statistics, Machine Learning, Data Visualization, Feature Engineering, Model Evaluation, Problem Solving, Communication) & Live Code Tracing
Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving
Database Schemas, APIs & Architecture Basics
Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion
Barclays Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
What is the difference between WHERE and HAVING clauses in SQL? Answer ▼
Model Answer: WHERE filters rows before any groupings are applied, while HAVING filters aggregated groups after GROUP BY has executed.
Explain SQL Window functions: ROW_NUMBER(), RANK(), and DENSE_RANK(). Answer ▼
Model Answer: ROW_NUMBER() assigns unique sequential integers. RANK() assigns identical ranks to ties and skips ranks. DENSE_RANK() assigns identical ranks to ties without skipping rank numbers.
How do you handle NULL and missing values during data cleaning in Python/Pandas? Answer ▼
Model Answer: Use .isna().sum() to identify missing values. Impute with mean/median using .fillna() or remove with .dropna(subset=[...]) depending on variance impact.
What is the difference between Star Schema and Snowflake Schema in Data Warehousing? Answer ▼
Model Answer: Star Schema has denormalized dimension tables directly connected to the central Fact table. Snowflake Schema normalizes dimension tables into sub-dimensions to minimize redundancy.
How do you calculate MoM (Month-over-Month) growth in SQL? Answer ▼
Model Answer: Use LAG(revenue, 1) OVER (ORDER BY month) to fetch the previous month's revenue and compute (revenue - prev_revenue) / prev_revenue * 100.
Why do you want to join Barclays as a AI/ML Data Scientist?
Preparation Tip: Highlight Barclays's market reputation, recent tech innovations, and how your skills in Python directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Preparation Tip: Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Preparation Tip: Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.

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