Description

Key Responsibilities

• Lead the end-to-end delivery of data science and data engineering projects, ensuring high-quality outcomes and timely execution.
• Manage, mentor, and support a team of approximately five data scientists and data engineers.
• Partner with client stakeholders to understand business objectives and translate them into scalable analytical solutions.
• Design and implement end-to-end data and machine learning solutions, including data ingestion, transformation, model development, evaluation, and deployment.
• Develop and review production-quality Python and SQL code while establishing engineering best practices for testing, documentation, and code quality.
• Guide the development of statistical and machine learning models for business use cases such as customer segmentation, forecasting, fraud detection, propensity modeling, and risk analytics.
• Ensure adherence to data governance, security, privacy, and regulatory requirements applicable to financial data.
• Support solution estimation, resource planning, proposal development, and identification of new business opportunities.

Required Qualifications

• Experience delivering data science solutions within banking, payments, financial services, lending, or other regulated industries.
• Expert-level proficiency in Python for data science and engineering using libraries such as pandas, scikit-learn, and related tools.
• Advanced SQL skills with experience working on large-scale analytical datasets and performance optimization.
• Demonstrated experience leading end-to-end analytics or machine learning projects from design through deployment.
• Experience leading or mentoring technical teams while managing project delivery and stakeholder expectations.
• Strong communication and presentation skills with the ability to engage technical and business stakeholders.
• Bachelor's or Master's degree in Computer Science, Statistics, Engineering, Mathematics, or a related quantitative discipline, or equivalent practical experience.

Preferred Qualifications

• Hands-on experience with AWS services such as S3, Glue, EMR, Redshift, SageMaker, or similar cloud technologies.
• Experience within the payments ecosystem, including issuers, acquirers, payment networks, or merchants.
• Knowledge of modern data engineering practices including Spark, orchestration frameworks, data modeling, and CI/CD pipelines.
• Experience with MLOps, feature stores, and production model monitoring.
• Prior consulting or client-facing delivery experience

Key Skills

Education

Bachelor's or Master's degrees