Lead Data Scientist (SMTS)

Join Salesforce's Cyber Security Operations Center as a Lead Data Scientist. In this role, you will leverage AI, machine learning, and contemporary data techniques to enhance the security of the Salesforce platform. You will collaborate with various teams, conduct exploratory data analyses, and drive the engineering of scalable, production-ready software. The ideal candidate will have 8+ years of industry experience in applied data science, machine learning, or AI engineering, along with deep expertise in applied statistics and advanced machine learning techniques.

- Conduire et documenter des analyses exploratoires rigoureuses des données sources potentielles. - Collaborer avec les équipes internes de réponse aux incidents, de renseignement sur les menaces et de produits sur les menaces évolutives et leur détection. - Formuler, construire, tester et documenter des preuves de concept pour de nouvelles techniques de détection des menaces.

- Proven track record of guiding cross-functional technical decisions and mentoring engineering and data science team members - 8+ years of industry experience in applied data science, machine learning, or AI engineering (or 6+ years with a relevant Master’s or Ph.D.) - Deep expertise in applied statistics, probability, and advanced machine learning techniques (deep learning, sequence modeling, anomaly detection, NLP/LLMs) - Production-level programming skills in Python, Java, or Scala, along with fluency in big data frameworks (e.g., Spark, PySpark, Flink) and modern MLOps ecosystems - Exceptional strategic communication skills, with the ability to influence technical roadmaps and articulate complex concepts to leadership - Demonstrated experience architecting and deploying ML pipelines and models over large-scale datasets (multi-terabyte or petabyte scale) - 3+ years in a technical leadership capacity (e.g., Lead Data Scientist, Tech Lead, or Principal Individual Contributor) owning project lifecycles from ideation to production - Hands-on experience building and deploying agentic AI systems, automated reasoning, or generative AI workflows in high-consequence operational environments - Experience in other high-throughput, low-latency domains involving pattern recognition and behavioral analysis (e.g., quantitative finance, high-frequency trading, genomics, or fraud detection) - Domain expertise in Cybersecurity, specifically in threat propagation, signal correlation, security log analysis (SIEM, EDR, network flows), and behavioral threat modeling