Data Science & ML-Ops Team Lead

We offer the industry’s only platform that fuses customer identity and anti-fraud solutions – customer identity management, identity verification, and fraud prevention. We sell to industries with large, consumer-facing businesses such as: banking, financial services, insurance, fintech, gaming, ecommerce/retail, telco / media, utilities, etc. About the Role: Transmit Security is building the next generation of Fraud Prevention and Detection & Response capabilities powered by machine learning, real-time decisioning, and large-scale data processing. 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 you’ll 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. What you’ll need: Lead, mentor, and grow a team of Data Scientists and Engineers, fostering a culture of technical excellence, ownership, and innovation. Drive the strategy, architecture, and roadmap for Machine-Learning and AI-powered Detection & Response capabilities. Design, train, evaluate, and optimize machine learning models for fraud prevention, risk assessment, and anomaly detection. Own the end-to-end ML lifecycle, including feature engineering, experimentation, deployment, strict monitoring, and continuous improvement. Build and scale ML platforms, tooling, and MLOps practices to enable reliable, efficient, and reproducible model development and operations. Build low-latency, production-grade inference services and scalable distributed systems. Collaborate closely with Product, Engineering, Security, and Customer teams to deliver impactful AI solutions and measurable business outcomes. Advantages: Experience with fraud detection, identity security, cybersecurity, risk engines, or behavioral analytics. Experience designing low-latency inference architectures and real-time decisioning systems. Experience building ML platforms and internal AI tooling. Experience with Kubernetes, Docker, Kafka, Spark, Airflow, Flink, or similar distributed systems technologies. Experience with feature stores, vector databases, model registries, and modern MLOps platforms. E