Lead Data Scientist

Job description

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.