Lead Data Scientist

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.
• Strong 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.