Data Scientist
First seen by Alion on Sep 8, 2026.
About the Role :
We are looking for a Data Scientist with experience in building and deploying machine learning models focused on behavioral signals, risk analytics, fraud detection, and underwriting. The ideal candidate should have experience working with large-scale customer, transaction, and lending datasets to derive actionable insights and improve decision-making across risk and credit functions.
Candidates from BFSI, FinTech, Payments, Digital Lending, NBFCs, or Banking organizations will be preferred.
Key Responsibilities :
- Develop and deploy predictive models for credit risk, underwriting, fraud detection, and customer behavior analysis.
- Identify and engineer behavioral signals from customer, transaction, device, and engagement data.
- Build risk scorecards and machine learning models to improve underwriting and credit decisioning.
- Analyze large datasets to identify trends, patterns, and risk indicators.
- Collaborate with Product, Risk, Credit, and Business teams to translate business problems into data science solutions.
- Design and evaluate model performance using statistical techniques and machine learning methodologies.
- Conduct A/B testing and model validation exercises.
- Monitor model performance and recommend enhancements to improve prediction accuracy.
- Develop dashboards, reports, and insights to support business decision-making.
- Ensure data quality, governance, and compliance with regulatory requirements.
Required Skills & Qualifications :
- 2 - 4 years of hands-on experience in Data Science, Machine Learning.
- Strong understanding of behavioral signals, risk modeling, underwriting, fraud analytics, or credit scoring.
- Experience working with customer, transactional, lending, or financial datasets.
- Proficiency in Python and SQL.
- Strong knowledge of machine learning algorithms, statistical modeling, and predictive analytics.
- Experience with data manipulation and analysis libraries such as Pandas, NumPy, Scikit-learn, and related tools.
- Experience building and evaluating classification, regression, and risk prediction models.
- Strong analytical and problem-solving skills.
Preferred Background :
- Experience in BFSI, FinTech, Payments, Banking, NBFCs, Digital Lending, or InsurTech organizations.
- Exposure to credit underwriting, loan origination, collections analytics, or fraud detection.
- Experience working with alternative data sources and behavioral data signals.
- Familiarity with cloud platforms and modern data ecosystems is a plus.
Skills
Data Science, Machine Learning, SQL, Python, Quantitative Risk Management, Fraud Analytics, Pandas, Numpy