Data Science Team Lead (AI)

TL;DR

Data Science Team Lead (AI): Leading a team of data scientists and ML engineers to build fraud prevention and detection capabilities, powered by advanced machine learning and real-time decisioning with an accent on real-time model serving and customer-specific model tuning. Focus on building scalable ML systems in a production-grade SaaS environment and improving offline AI evaluation frameworks.

Location: Tel Aviv-Yafo, Gush Dan, Israel

Company

hirify.global offers a platform that fuses customer identity and anti-fraud solutions, including customer identity management, identity verification, and fraud prevention.

What you will do

• Lead and mentor a team of Data Scientists and ML Engineers focused on fraud detection and response capabilities.
• Build ML infrastructure focused on design, train, evaluate, and optimize machine learning models for real-time fraud prevention and risk assessment.
• Own the lifecycle of ML models in production, including experimentation, deployment, monitoring, retraining, and performance optimization.
• Drive customer-specific model training and tuning strategies to improve accuracy and adaptability across different customer environments.
• Build and improve offline AI evaluation frameworks to measure model quality, drift, effectiveness, and business impact.
• Collaborate closely with Engineering, Product, Security, and Data teams to deliver scalable and reliable AI-powered capabilities.

Requirements

• 5+ years of experience in Data Science, Machine Learning, or Applied AI roles, with at least 2 years in a leadership capacity.
• Strong hands-on experience building and deploying ML models in production environments.
• Experience with real-time inference/model serving architectures and low-latency prediction systems.
• Deep understanding of model training, evaluation, tuning, and monitoring methodologies.
• Experience designing customer-specific ML solutions and personalization strategies.
• Strong programming skills in Python and experience with modern ML frameworks and tooling.

Nice to have

• Experience with fraud detection, identity risk, cybersecurity, or behavioral analytics systems.
• Experience with MLOps practices and tooling.
• Background in Data Engineering and large-scale data processing systems.
• Experience with feature stores, stream processing, and real-time data pipelines.
• Familiarity with cloud platforms such as AWS, GCP, or Azure.

Culture & Benefits

• Promote a culture of technical excellence, continuous improvement, ownership, and innovation.