Key Responsibilities:

• Lead the end-to-end development of fraud detection ML models - from ideation and feature engineering through training, evaluation, and production ownership
• Build and grow a new DS team, fostering a strong research-oriented and data-driven culture
• Own model performance in production, monitoring degradation and driving timely responses
• Collaborate cross-functionally with Data Engineering, ML Engineering, DevOps, and Product
• Lead complex, cross-functional projects in a matrix work environment, coordinating team members across departments
• Share knowledge and mentor team members to elevate the team's collective capabilities

Requirements:

• 2–5 years of hands-on experience as a Data Scientist, with or without prior team leadership experience
• Strong proficiency in Python and core ML/data libraries (pandas, numpy, scikit-learn and similar frameworks)
• Solid SQL skills and experience working with large datasets in production
• Hands-on experience with feature engineering and model evaluation (AUC, precision, recall)
• Experience working in cloud/production environments
• Proficiency in leveraging LLM-based tools as part of day-to-day work
• Excellent interpersonal and communication skills
• Proactive problem-solver with a can-do approach
• Ability to influence without authority and drive alignment across teams
• Proven ability to lead in a matrix environment — managing projects with contributors from multiple teams
• Demonstrated ability to lead and develop team members, with a genuine passion for mentoring and knowledge sharing

Nice to Have

• Background in fraud detection or identity verification
• Hands-on experience with Azure (ML Studio, Data Factory, or similar services)
• Experience with MLOps practices and tooling