Data Science & ML-Ops Team Lead

We offer the industrys 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 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.

What youll 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.
• Experience with AWS GCP or Azure.
• Familiarity with LLMs GenAI applications AI evaluation frameworks and agentic systems.
• Background in Data Engineering Platform Engineering or Backend Engineering.
• Experience operating mission-critical systems with strict latency and availability requirements.
• . or higher degree in Computer Science Engineering Mathematics Statistics or a related field.

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