תיאור המשרה
Founded in 2002, AU10TIX is the global leader in AI-driven identity verification and management, protecting the world's largest brands against advanced fraud. The company's future-proof product portfolio helps businesses provide frictionless customer onboarding and verification in 4–8 seconds while staying ahead of emerging threats and evolving regulatory requirements.
We are looking for a talented and driven Data Scientist - whether you're an experienced DS Team Leader drawn to the challenge of building a team from scratch, or a strong individual contributor ready to take the next step into leadership. In this role, you will build and lead a new DS team at the heart of our Fraud Intelligence Hub, owning the full ML lifecycle - from research and feature engineering through model training, evaluation, and production performance. You will work closely with Data Engineering, ML Engineering, DevOps, and Product teams to ensure that models are not only technically sound but deliver measurable real-world impact.
This is a unique opportunity to shape a team from the ground up, define its ways of working, and drive the development of core fraud detection models in a fast-moving, high-stakes domain.
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