T
Data Science and MLOps Team Lead
On-siteFull-timeAI & Machine LearningCybersecurityEnterprise Software
Lead a multidisciplinary team building production-grade machine learning systems for fraud prevention, risk assessment, and real-time detection and response.
Responsibilities
- Lead and mentor a multidisciplinary data science and ML engineering team.
- Own end-to-end machine learning system delivery from data and feature engineering through production operation.
- Develop fraud detection and risk assessment models for real-time decisioning.
- Create evaluation frameworks for model quality, drift, effectiveness, and business impact.
- Define standards for model serving, experimentation, observability, and operational excellence.
- Balance model performance, latency, scalability, explainability, and operational constraints.
- Drive technical excellence, ownership, continuous improvement, and innovation.
Requirements
- Lead, mentor, and develop data scientists and ML engineers.
- Define strategy, architecture, and roadmaps for AI-powered detection and response capabilities.
- Design, train, evaluate, and optimize machine learning models for fraud prevention, risk assessment, and anomaly detection.
- Own the model lifecycle, including feature engineering, experimentation, deployment, monitoring, retraining, and optimization.
- Build and scale ML platforms, tooling, and MLOps practices for reliable and reproducible model operations.
- Build low-latency inference services and scalable distributed systems.
- Collaborate with product, engineering, security, data, and customer teams to deliver reliable AI capabilities.
Nice to have
- Experience in fraud detection, identity security, cybersecurity, risk engines, or behavioral analytics.
- Experience with low-latency inference and real-time decisioning systems.
- Experience building ML platforms and internal AI tooling.
- Experience with Kubernetes, Docker, Kafka, Spark, Airflow, Flink, feature stores, vector databases, model registries, or modern MLOps platforms.
- Experience with AWS, GCP, or Azure.
- Familiarity with LLMs, generative AI, AI evaluation frameworks, or agentic systems.
- Background in data engineering, platform engineering, or backend engineering.
- Experience operating mission-critical systems with strict latency and availability requirements.
- Bachelor’s degree or higher in computer science, engineering, mathematics, statistics, or a related field.