Senior Data Scientist

What you'll do

• Develop and manage the lifecycle of security ML models, including feature engineering, testing, deployment, and ongoing improvement.
• Build data pipelines and evaluation frameworks, and apply research methods to emerging cyber threats.
• Partner with Security Operations, Product, and Data Engineering to make model outputs actionable and communicate findings.

What they ask for

• 5+ years of professional experience in Data Science, Machine Learning Engineering, or a related quantitative field.
• Strong theoretical and practical experience with ML models, plus proficiency in Python and its data science ecosystem.
• Expertise in SQL and large-scale data warehouses; experience with MLOps principles, tools, and platforms.
• Understanding of probability, statistics, and experimental design; experience deploying and maintaining models in GCP or AWS.

Nice to have

• Prior data science or ML experience in cybersecurity, security analytics, MDR, or MSSP environments.
• Experience with streaming data systems, vector databases, RAG architectures, modern analytics engineering frameworks, or early-stage startups.

Tools

Python, Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow, SQL, Snowflake, BigQuery

AI focus

Develops and deploys ML for threat detection, anomaly scoring, behavioral analytics, and alert triage; also uses vector databases and RAG pipelines for security context and large language model applications.