LayerX Security is on the lookout for a Data Scientist who blends applied machine learning, research, and production awareness to build models that protect users and drive product innovation. You’ll work at the core of how we interpret signals from the browser, detect threats, and automate risk scoring for our platform. This role partners closely with Product, Security Research, and engineering teams to transform data science into real customer impact - with models deployed and maintained in production.

LayerX Security:

LayerX’s user-first browser security platform turns any browser into the most protected & manageable workspace, by providing real-time monitoring and governance over users’ activities on the web, protecting the enterprise’s applications, data, and devices from web-borne risks. All with near-zero impact on user experience.

Our Engineering Values:

• Customer First
• Engineering Excellence
• Continuous Improvement
• Shared Accountability
• Strong Ownership
• Challenge Anything
  
  

Responsibilities:

• Design and implement applied ML models (classification, scoring, anomaly detection) that address security and product needs.
• Conduct research-heavy work to explore novel approaches that don’t yet exist in the team.
• Collaborate with ML engineers and DevOps to ensure models are production-ready, scalable, and meet performance standards.
• Build and manage automated retraining and monitoring pipelines for deployed models.
• Translate business and security problems into robust data science solutions.
• Provide clear and relevant insights to stakeholders, using strong data storytelling.
• Influence product and roadmap decisions with data-backed model outputs and insights
  
  

Requirements:

• 4+ years experience in data science, ML, or related role.
• Strong command of Python and ML tooling.
• Experience with at least one production-scale ML framework (e.g., TensorFlow, PyTorch, Spark MLlib).
• Familiarity with ML lifecycle tooling (DVC, MLflow, Weights & Biases, etc.) and best practices for model versioning and tracking.
• Prior experience in cybersecurity or security-oriented modeling.
• Solid understanding of model evaluation, performance constraints, and deployment considerations.
• Comfortable working with cross-functional stakeholders and explaining technical concepts clearly.
  
  

Advantages:

• Experience with big data frameworks and distributed environments.
• Knowledge of BI/dashboard tools and data pipelines.
• Publications, talks, or contributions to ML research communities.
• Practical experience setting up CI/CD for ML models or automated monitoring.
• Experience with cloud platforms (AWS/GCP/Azure).