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Metro location, generalist Data Scientist title, and broad ML/AWS requirements increase applicant competition.
ML engineering skills are transferable, though fraud/payments domain experience is moderately preferred.
Explicit 2–3 year requirement plus mandatory ML, AWS, and MLOps skills make filters stringent.
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Develop, validate, deploy, and monitor machine learning models for fraud detection and transaction risk across multiple money services products.
Manage the full model lifecycle including feature engineering, governance, and performance benchmarking to modernize fraud risk infrastructure.
Collaborate with risk and compliance teams to develop data-driven fraud prevention policies and deliver actionable insights via Power BI dashboards and reports.
2-3 years of experience in data science or related field with a focus on fraud risk analytics.
Proficiency in Python (Pandas, Scikit-learn, XGBoost, TensorFlow/PyTorch) and SQL for data analysis.
Experience with AWS cloud services (e.g., S3, SageMaker, Lambda), model deployment, and MLOps practices including version control with Git.
Location: Gurugram, India; Business fluent English communication skills required.
Experienced in leading analytics or machine learning projects with end-to-end ownership in fraud detection or financial transaction risk domains.
Strong ability to translate complex data insights into clear, actionable business policies and communicate results to both technical and non-technical stakeholders.
Comfortable working in a cross-functional environment, providing guidance to juniors and collaborating closely with risk, compliance, and technology teams.