Data Science & ML-Ops Team Lead

<p>We offer the industry’s only platform that fuses customer identity and anti-fraud solutions – customer identity management, identity verification, and fraud prevention.&nbsp;</p> <p>We sell to industries with large, consumer-facing businesses such as: banking, financial services, insurance, fintech, gaming, ecommerce/retail, telco / media, utilities, etc.</p> <p>&nbsp;</p> <p><strong>About the Role:</strong></p> <p>Transmit Security is building the next generation of Fraud Prevention and Detection &amp; Response capabilities powered by machine learning, real-time decisioning, and large-scale data processing.</p> <p>We are looking for a Data Science &amp; 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.</p> <p>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.</p> <p>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.</p> <p>If you are passionate about building intelligent systems at scale and transforming machine learning into reliable production services, we want to meet you.</p> <p>&nbsp;</p> <p><strong>What you’ll do:</strong></p> <ul> <li>Lead and mentor a team of Data Scientists and ML Engineers focused on fraud detection and response capabilities.</li> <li>Build ML infrastructure focused on design, train, evaluate, and optimize machine learning models for real-time fraud prevention and risk assessment.</li> <li>Own the lifecycle of ML models in production, including experimentation, deployment, monitoring, retraining, and performance optimization.</li> <li>Drive customer-specific model training and tuning strategies to improve accuracy and adaptability across different customer environments.</li> <li>Build and improve offline AI evaluation frameworks to measure model quality, drift, effectiveness, and business impact.</li> <li>Collaborate closely with Engineering, Product, Security, and Data teams to deliver scalable and reliable AI-powered capabilities.</li> <li>Define best practices for model serving, feature engineering, experimentation, observability, and operational excellence.</li> <li>Balance model performance, latency, scalability, explainability, and operational constraints in high-scale production environments.</li> <li>Promote a culture of technical excellence, continuous improvement, ownership, and innovation.</li> </ul> <p>&nbsp;</p> <p><strong>What you’ll need:</strong></p> <ul> <li>Lead, mentor, and grow a team of Data Scientists and Engineers, fostering a culture of technical excellence, ownership, and innovation.</li> <li>Drive the strategy, architecture, and roadmap for Machine-Learning and AI-powered Detection &amp; Response capabilities.</li> <li>Design, train, evaluate, and optimize machine learning models for fraud prevention, risk assessment, and anomaly detection.</li> <li>Own the end-to-end ML lifecycle, including feature engineering, experimentation, deployment, strict monitoring, and continuous improvement.</li> <li>Build and scale ML platforms, tooling, and MLOps practices to enable reliable, efficient, and reproducible model development and operations.</li> <li>Build low-latency, production-grade inference services and scalable distributed systems.</li> <li>Collaborate closely with Product, Engineering, Security, and Customer teams to deliver impactful AI solutions and measurable business outcomes.</li> </ul> <h2><span style="font-size: 10pt;"><strong>Advantages:</strong></span></h2> <ul> <li>Experience with fraud detection, identity security, cybersecurity, risk engines, or behavioral analytics.</li> <li>Experience designing low-latency inference architectures and real-time decisioning systems.</li> <li>Experience building ML platforms and internal AI tooling.</li> <li>Experience with Kubernetes, Docker, Kafka, Spark, Airflow, Flink, or similar distributed systems technologies.</li> <li>Experience with feature stores, vector databases, model registries, and modern MLOps platforms.</li> <li>Experience with AWS, GCP, or Azure.</li> <li>Familiarity with LLMs, GenAI applications, AI evaluation frameworks, and agentic systems.</li> <li>Background in Data Engineering, Platform Engineering, or Backend Engineering.</li> <li>Experience operating mission-critical systems with strict latency and availability requirements.</li> <li>B.Sc. or higher degree in Computer Science, Engineering, Mathematics, Statistics, or a related field.</li> </ul> <p>#LI-AM1 #LI-Hybrid&nbsp;</p> <p>&nbsp;</p> <p>#LI-TL1 #LI-Hybrid&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p>