ExperianRisk Analyst – Data Science & Analytics
Experian is hiring a Risk Analyst – Data Science & Analytics for a remote role open to applicants in India. Level rates it ; you can apply on Experian.
Level's read on this job
- Level rates this role Works on AI ●●●○: the daily work is on AI products, without building the model.
- The posting lists this role as remote.
- Tools and skills the posting cites include Databricks, Python and SQL.
- Level lists 390 other open roles at Experian.
- The role was posted in the last few days.
AI in this role
Develop predictive models, machine learning solutions, and data analytics products for risk assessment.
Experian is a global data and technology company, powering opportunities for people and businesses around the world. We operate across a range of markets, from financial services to healthcare, automotive, agribusiness, insurance, and many more. Experian invests in people and new advanced technologies to unlock the power of data. We have an amazing team of 25,200 people in 32 countries.
We are looking for a Risk Analyst – Data Science & Analytics to join our Analytics Product & Innovation team. In this hands-on role, you'll leverage bureau data, statistics, and machine learning to develop and enhance data-driven products. You'll work across the analytics lifecycle, from use-case exploration and prototyping to validation, UAT, and productisation. We're looking for a strong coder and analytical thinker who enjoys solving complex problems with data and building scalable solutions that deliver client impact.
What you'll do
- Analyze large and complex datasets to uncover insights, identify opportunities, and support the development of innovative analytics products.
- Transform business challenges and product ideas into data-driven solutions through exploratory analysis, feature engineering, statistical modelling, and machine learning.
- Develop, validate, and benchmark predictive models, scores, segmentations, and decision-support solutions, ensuring strong performance, stability, interpretability, and business impact.
- Build scalable and reusable analytical assets using Python and SQL, including automated workflows, utilities, and frameworks that enable repeatable product development.
- Collaborate with Product, Technology, and business teams to define requirements, support UAT, investigate issues, and drive the successful delivery and enhancement of analytics products.
- Communicate analytical insights and methodologies clearly through robust documentation while maintaining high standards for data security, model governance, compliance, and quality assurance.
- 3+ years of experience in Data Science, Analytics, Decision Science, Statistical Modelling, or a related quantitative field, applying data-driven approaches to solve complex business problems.
- Strong hands-on expertise in Python and SQL, with experience working on large datasets for data preparation, feature engineering, exploratory analysis, modelling, and insight generation.
- Solid foundation in statistics, machine learning, and model development, including regression, classification, clustering, ensemble methods, model validation, performance evaluation, and interpretability.
- Proven ability to transform business questions into structured analytical solutions, communicate insights effectively, and influence decisions across technical and non-technical stakeholders.
- Strong focus on data quality, analytical rigor, reproducible coding, documentation, testing, and best practices throughout the analytics lifecycle.
- Experience with modern analytics ecosystems such as Git, SAS, Spark, Databricks, cloud platforms, workflow automation, model monitoring, governance, and productisation. Exposure to lending, credit risk, bureau, fraud, portfolio, or decision analytics is beneficial but not required.
Domain Note: We welcome candidates from diverse analytics backgrounds. While experience in credit risk or bureau analytics is valued, we primarily seek professionals with strong analytical thinking, coding expertise, statistical reasoning, and a passion for building impactful, data-driven solutions.
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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How we rate this
Risk Analyst – Data Science & Analytics at Experian rates 75 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where statistical modeling was part of your work. What did you do?
- Tell me about a project where feature engineering was part of your work. What did you do?
- Tell me about a project where predictive modeling was part of your work. What did you do?
- Walk me through how you've used Databricks in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: Machine learning, Statistical Modeling, Feature Engineering, Predictive Modeling, and Databricks. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
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