Job Description

We are looking for a Risk Analyst - Data Science & Analytics to join our Market Insights & Custom Analytics team. This is a hands-on analytics role focused on turning large and complex datasets into high-quality market intelligence, client-specific insights and analytical solutions. You will work across exploratory analysis, statistical modelling, segmentation, benchmarking, customer and portfolio analytics, and automation of repeatable insight workflows. The role requires strong coding and analytical depth together with the ability to explain what the data means for a business audience prior experience in a specific risk domain is not mandatory.

What you'll do

Analyse large bureau and client datasets to identify market trends, portfolio patterns, customer segments, emerging risks and growth opportunities.
Deliver custom analytics assignments by converting client questions into structured hypotheses, analytical methods, outputs and recommendations.
Build statistical models, segmentations, benchmarks and diagnostic analyses where they materially improve the quality of insight or decision making.
Perform data preparation, exploratory analysis, feature engineering and quality checks using efficient Python and SQL code.
Develop repeatable analytical datasets, code libraries and automated workflows to improve the speed and consistency of recurring market-insight outputs.
Create clear, decision-oriented charts, tables and narratives that communicate analytical findings without overstating what the data supports.
Contribute analytical content to industry insight reports, client presentations and other thought-leadership outputs.
Work with stakeholders to refine requirements, validate findings and ensure the final output addresses the intended business question.
Maintain best practices for analytical accuracy, documentation, reproducibility and peer review.
Follow applicable data-security, governance and compliance requirements.

What success looks like

Accurate, insightful and custom analytics and market-insight deliverables.
Outputs that translate data into clear business implications and are useful to clients and internal stakeholders.
Increasing automation and reuse across recurring reports, benchmarks and analytical workflows.
Strong analytical quality and low rework through disciplined validation and documentation.

Qualifications

What you'll need to bring

Approximately 3+ years of experience in analytics, data science, decision science, statistical modelling or a closely related field.
Strong hands-on proficiency in Python for data manipulation, exploratory analysis, statistical modelling and machine learning.
Strong working knowledge of SQL, including the ability to extract, transform and analyse large and complex datasets.
Sound grounding in applied statistics, including sampling, distributions, hypothesis testing, regression and model evaluation.
Hands-on experience with common supervised and unsupervised techniques such as regression, tree-based methods, ensemble methods, gradient boosting, classification, clustering and segmentation.
Understanding of model-development practices including train/validation/test design, cross-validation, overfitting, performance metrics, stability and interpretability.
Strong data-wrangling, data-quality assessment, feature-engineering and analytical validation skills.
Convert a business question into a structured analytical approach and communicate findings clearly to technical and non-technical stakeholders.
A disciplined approach to coding, documentation, reproducibility and quality assurance.

Good to have

SAS or another statistical programming environment.
Git or similar version-control tools and collaborative coding practices such as peer review.
Cloud analytics platforms, distributed computing or tools such as Spark / Databricks.
Experience automating analytical workflows or developing reusable analytics libraries and utilities.
Exposure to productionisation, model monitoring or model-governance frameworks.
Exposure to customer analytics, portfolio analytics, market intelligence, risk analytics, segmentation or benchmarking.
Experience developing analytical visualisations, dashboards or publication-quality charts using Python or BI tools.
Experience in banking, lending, fintech, payments, insurance, credit-bureau data or other data-rich industries.
Experience presenting analytical findings or supporting client-facing deliverables.

Domain note: Experience in credit risk or bureau analytics is valuable, but strong analytics, coding and statistical reasoning are the primary selection criteria.

Additional Information

Our uniqueness is that we celebrate yours. Experian's people first, inclusive and purpose driven culture is multi award-winning World's Best Workplaces™ 2025 (Fortune Global Top 25), Great Place To Work™ in 26 countries to name a few. Check out Experian Life on social or explore our Careers Site to understand why. Experian is also proud to be an Equal Opportunity and Affirmative Action employer. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity.

Benefits:

Great compensation package and discretionary bonus plan
Core benefits include pension, health Insurance and term life Insurance, Sharesave scheme and more!
25 days annual leave with 13 bank holidays and 3 volunteering days. You can also purchase additional annual leave.
You will report to Senior Analytics Consultant
Role Location: Mumbai
Experian is an equal opportunities employer

Recruitment Fraud Awareness - Experian's recruitment process is conducted only through authorised channels. Recruitment communications will only be sent from an @experian.com email address.

Experian will never ask candidates to make any payment as part of an application, interview, assessment, onboarding, or recruitment process. To apply for roles or verify opportunities, please visit experian.com/careers.

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Risk Analyst – Data Science & Analytics