Description

Lead end-to-end delivery of data science and engineering projects, managing a team of five while partnering with client stakeholders to build scalable analytical solutions.

This role is on-site.

Responsibilities

• Design and implement end-to-end data and machine learning pipelines, covering ingestion, transformation, model development, and deployment.
• Manage and mentor a team of data scientists and engineers, ensuring high-quality outcomes and timely execution.
• Develop and review production-quality Python and SQL code, establishing engineering best practices for testing and documentation.
• Guide the creation of statistical and ML models for use cases such as fraud detection, forecasting, and risk analytics.
• Ensure adherence to data governance, security, privacy, and regulatory requirements for financial data.

Required Skills

• Expert-level proficiency in Python for data science and engineering (pandas, scikit-learn).
• Advanced SQL skills with experience optimizing large-scale analytical datasets.
• Demonstrated experience leading end-to-end analytics or ML projects from design through deployment.
• Experience leading technical teams and managing stakeholder expectations in regulated industries (banking, payments, financial services).
• Bachelor's or Master's degree in Computer Science, Statistics, Engineering, Mathematics, or related quantitative discipline.
• Strong communication skills to engage technical and business stakeholders.

Preferred Skills

• Hands-on experience with AWS services (S3, Glue, EMR, Redshift, SageMaker).
• Knowledge of modern data engineering practices including Spark, orchestration, and CI/CD.
• Experience with MLOps, feature stores, and production model monitoring.

Key Skills

Education

Bachelor's or Master's degrees