Risk Analyst – Data Science & Analytics
Experian (company site, opens in a new tab)3+ yrsMumbaiOn-siteDirect career pageEst. 12–22 LPA
Posted 18h ago
Reach people at Experian
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Experian you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Develop and improve analytics products using bureau data, statistical modelling and machine learning, from exploration and prototyping through validation, UAT and productization.
- Build and benchmark predictive models, scores and decision-support solutions; ensure performance, stability, interpretability and business impact.
- Create reusable Python/SQL workflows and partner with Product, Technology and business teams to deliver and enhance products, with documentation and governance.
Minimum Requirements
- Work Experience Required: 3+ years in Data Science, Analytics, Decision Science, Statistical Modelling or a related quantitative field.
- Hands-on Python and SQL experience, including work with large datasets; solid statistics and machine-learning foundation covering model development, validation and evaluation.
- Role location: Mumbai; onsite (#LI-Onsite).
Ideal Candidate Profile
- Has applied analytics to complex business problems and can translate product or business questions into structured modelling solutions.
- Can work across the analytics lifecycle, including feature engineering, validation, UAT, reusable workflows and productization.
- Credit risk, lending, bureau, fraud, portfolio or decision analytics experience is beneficial but not required; exposure to tools such as Git, SAS, Spark, Databricks or cloud platforms is also beneficial.