Key Skills: Risk Analytics, Data Scientist, Fraud Analysis, Python, Machine Learning, SQL
Roles and Responsibilities:
Develop fraud detection, anomaly detection, and risk scoring models for high-volume transaction and account activity.
Design feature engineering and validation approaches for structured, time-based, and relationship-driven fraud signals.
Build and maintain ETL/ELT pipelines that support model training, scoring, and analytics workflows.
Integrate REST APIs and external data sources into production analytics and decisioning processes.
Partner with engineering and risk teams to operationalize models, monitor outcomes, and improve model quality over time.
Skills Required:
6-10 years of experience in data science, analytics engineering, machine learning, or a closely related discipline.




Advanced hands-on experience in Python and SQL for data science and analytics engineering work.
Solid background in fraud analysis, risk analytics, machine learning, and statistical modeling.
Experience with production-grade data pipelines, model validation, and model monitoring on large datasets.
Ability to support API integrations, CI/CD practices, and cloud-based analytical environments.
Good to Have:
Snowflake, PostgreSQL, Neo4j, Cypher, MLOps, Azure, AWS, Kubernetes, observability tools, and fraud case-management experience.
Education: Bachelor's degree in data science, computer science, engineering, statistics, mathematics, operations research, or a related quantitative field; Master's degree preferred.

📌 Lead Data Scientist_102571 (Chennai)
🏢 MyCareernet
📍 Chennai