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:

• 6 years of experience in Machine Learning Data Science Software Engineering or Applied AI roles.
• 2 years of experience leading technical teams.
• Strong hands-on software engineering experience building production systems and scalable services.
• Proven experience deploying operating and maintaining ML models in production environments.
• Deep understanding of MLOps principles model lifecycle management and production AI systems.
• Strong programming skills in Python and experience with modern software engineering practices.
• Experience building APIs backend services and distributed systems.
• Strong understanding of feature engineering model evaluation experimentation and observability.
• Experience working with cloud-native architectures and production SaaS environments.
• Excellent communication leadership and cross-functional collaboration skills.

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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