Why This Job is Featured on The SaaS Jobs
Fraud prevention and identity are increasingly core SaaS platform capabilities, especially for vendors serving consumer-facing enterprises. This Data Science Team Lead role sits at that intersection, focusing on real-time decisioning and detection and response, where model accuracy and latency directly shape product outcomes. The scope implies a production-grade SaaS environment with broad applicability across regulated and high-volume industries.
From a SaaS career perspective, the work builds durable experience in operating machine learning as a product feature rather than a research function. Ownership across experimentation, deployment, monitoring, retraining, and evaluation aligns with how modern SaaS companies industrialise ML through MLOps and observability. Exposure to customer-specific tuning also reflects a common SaaS reality: models must generalise while adapting to varied tenant data and risk profiles.
This role tends to suit a leader who remains hands-on while guiding a team and setting technical direction. It will appeal to professionals who prefer cross-functional delivery with Engineering, Product, Security, and Data, and who are comfortable making trade-offs among performance, explainability, and operational constraints. It also fits someone motivated by measurable impact in high-stakes, real-time systems.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
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 advanced machine learning and real-time decisioning.
As the Data Science Team Lead, you will lead a talented team of data scientists and ML engineers building the infrastructure, systems, and workflows for designing, training, evaluating, and deploying machine learning models that protect millions of users worldwide from fraud and account compromise.
This role combines hands-on technical leadership with people management and strategic ownership. You will drive innovation across real-time model serving, customer-specific model tuning, offline AI evaluations, and scalable ML systems in a production-grade SaaS environment.
If you are passionate about applied machine learning, fraud detection, and building intelligent systems at scale - we want you on our team.
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:
- 5+ years of experience in Data Science, Machine Learning, or Applied AI roles, with at least 2 years in a leadership capacity.
- Strong hands-on experience building and deploying ML models in production environments.
- Experience with real-time inference/model serving architectures and low-latency prediction systems.
- Deep understanding of model training, evaluation, tuning, and monitoring methodologies.
- Experience designing customer-specific ML solutions and personalization strategies.
- Strong programming skills in Python and experience with modern ML frameworks and tooling.
- Proven ability to lead technical initiatives and guide teams in fast-paced, production-focused environments.
- Strong analytical and problem-solving skills with a data-driven mindset.
- Excellent communication and cross-functional collaboration skills.
Advantages:
- Experience with fraud detection, identity risk, cybersecurity, or behavioral analytics systems.
- Experience with MLOps practices and tooling.
- Background in Data Engineering and large-scale data processing systems.
- Experience with feature stores, stream processing, and real-time data pipelines.
- Familiarity with cloud platforms such as AWS, GCP, or Azure.
- Experience with Kubernetes, Kafka, Spark, Airflow, or similar distributed systems technologies.
- Bachelor’s degree in Computer Science, Mathematics, Statistics, Engineering, or a related field
#LI-AM1 #LI-Hybrid
#LI-TL1 #LI-Hybrid
