Data Science & ML-Ops Team Lead

We are looking for a Data Science & ML-Ops Team Lead to lead a multidisciplinary team of Data Scientists and ML Engineers responsible for designing, building, deploying, and operating production-grade machine learning systems.
This is a highly technical leadership role that combines applied machine learning understanding, software engineering, distributed systems, and MLOps. You will own the end-to-end lifecycle of our AI capabilities - from data and feature engineering to model training, deployment, monitoring, experimentation, and continuous improvement.
You will play a key role in defining the architecture, engineering standards, and operational practices behind fraud detection systems that protect millions of users globally in real time.
If you are passionate about building intelligent systems at scale and transforming machine learning into reliable production services, we want to meet you.
What youll 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.
Define best practices for model serving, feature engineering, experimentation, observability, and operational excellence.
Balance model performance, latency, scalability, explainability, and operational constraints in high-scale production environments.
Promote a culture of technical excellence, continuous improvement, ownership, and innovation.