Data Scientist

Role Overview

As an ML Engineer, you will design, develop, and deploy advanced machine learning models to detect and mitigate financial economic crime. You will act as a bridge between technical data engineering and business strategy, ensuring that data is adequate for complex modeling and that your models operate reliably in production. You will provide critical insights to senior management, helping to overcome technical and process impediments in the fight against financial crime.

Key Responsibilities:

• Data Stewardship: Partner with the data management team to evaluate data adequacy, ensuring the foundation for your models is robust and compliant.
• End-to-End ML Lifecycle: Design, develop, evaluate, and deploy production-grade machine learning models tailored for financial crime detection.
• Production Ownership: Oversee the full lifecycle of your models, including latency optimization, service-level agreements (SLAs), monitoring, and automated retraining workflows.
• Strategic Advisory: Advise senior leadership on key impediments, technical risks, and potential solutions to enhance crime detection capabilities.
• Stakeholder Management: Conduct discovery sessions to translate ambiguous business problems into measurable, high-impact machine learning deliverables.

Your Profile:

• Education: Master’s degree in Data Science, Engineering, or an equivalent field.
• Experience: 2+ years of professional experience applying ML to large datasets, with a proven track record of successful production deployments.
• Technical Stack:
• Languages: Strong proficiency in Python and SQL.
• Big Data: Advanced Spark skills.
• Platforms: Hands-on experience with Databricks and Azure DevOps (or equivalent cloud-native ML tooling).
• Operational Excellence: Solid understanding of model monitoring, data quality standards, and MLOps principles.
• Communication: Excellent communication skills, with the ability to work effectively within Agile/SCRUM teams.
• Bonus Qualifications: Experience in highly regulated industries such as Finance, Leasing, KYC (Know Your Customer), or AML (Anti-Money Laundering).