Senior Data Scientist

o Design, build, and iterate on AI‑ and agent‑based solutions that operate by default in Security product workflows. o Develop intelligent systems using ML, LLMs, and retrieval‑augmented approaches to automate analysis, decisioning, and insight generation. o Partner with engineering and PM to productionize agents and AI features with real customer and business impact. o Define success metrics and telemetry for AI agents and continuously improve them using feedback loops. Statistical Analysis & Experimentation o Design and execute controlled experiments to validate product and business hypotheses. o Apply advanced statistical techniques (e.g., regression, causal inference, Bayesian methods) to security and product data. o Clearly communicate uncertainty, limitations, and confidence to stakeholders. Model Development & Deployment o Develop predictive and prescriptive models using machine learning and AI techniques. o Ensure models and agents are production‑ready, scalable, and aligned with privacy, security, and compliance requirements. o Monitor performance post‑deployment and iterate using telemetry and user feedback. Business & Product Impact o Translate complex analytical and AI‑driven outputs into clear product and business recommendations. o Influence Security product strategy and prioritization through data and experimentation. o Collaborate cross‑functionally to align analytics, agents, and AI investments with organizational goals. Data Engineering & Infrastructure o Build ad‑hoc and production‑grade data pipelines over large‑scale security and product telemetry. o Partner with Data Engineering teams to ensure secure, reliable, and scalable data infrastructure. o Implement best practices for data quality, governance, and observability. o Coach and mentor junior data scientists on applied ML, experimentation, and AI‑first development. o Drive adoption of agent‑centric and AI‑native patterns across the team. o Contribute to standards for experimentation, metrics, and responsible AI usage. Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.

Programming:

Python, SQL (R optional)

Machine Learning & AI:

supervised/unsupervised learning, model evaluation, applied ML systems

Statistics:

experimentation, hypothesis testing, causal inference

Data Engineering:

Spark, data pipelines, Azure Data Lake Experience working with production data systems and distributed architectures. Ability to explain complex technical topics to non‑technical audiences. Experience building AI‑ or agent‑based systems, including LLM‑enabled workflows. Familiarity with ML/AI deployment pipelines and observability. Background working with security, trust, privacy, or compliance‑sensitive data. Ability to influence decisions through clear, concise storytelling with data and metrics.