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Risk Analyst – Data Science & Analytics
Experian
Mumbai, IND
Full-time
Mid · 3+ years experience
Onsite
Discovered 3 weeks ago
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Key Skills for This Role
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Full Job Posting
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.
- #LI-Onsite
- Experian Careers - Creating a better tomorrow together
- Find out what its like to work for Experian by clicking here
- 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.
Qualifications
- 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.
- 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.
About Experian
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