Staff Data Scientist - Trust and Safety
Develops ML models for fraud/abuse detection and anomalous activity on Databricks platform. Analyzes security features, collaborates cross-functionally, and deploys production solutions. Requires 7+ years experience, MS in quantitative field, Python/SQL/Spark expertise.
About the job
Impact You Will Have
- Develop and implement Machine Learning models to detect anomalous activity in products.
- Analyze the performance and pricing of security-related features and work with product and engineering teams to identify opportunities.
- Collaborate with security engineers, trust and safety experts, and machine learning engineers to build systems and tools that protect Databricks and customers from threats.
- Create solutions and frameworks to meet compliance requirements.
- Gather requirements, define project OKRs and milestones, and communicate progress to technical and non-technical audiences.
- Guide junior data scientists and interns on project planning, technical decisions, and code review.
- Represent the data science discipline organization-wide to drive data-driven decisions.
- Represent Databricks at academic and industrial conferences.
What We Look For
- 7+ years of data science, machine learning, and advanced analytics experience in high-velocity, high-growth companies.
- Understanding of good software engineering practices around testing, code reviews, and deployment.
- Experience working cross-functionally and communicating results to non-technical partners.
- Experience deploying Data Science / ML solutions in production.
- Coding skills in SQL and a software development language (Python preferred).
- Experience with distributed data processing systems like Spark and familiarity with software engineering principles.
- Prior experience applying machine learning and data analytics to identify SaaS product misuse and enhance compliance (preferred).
- Masters or higher in quantitative fields or equivalent industry experience.
Skills
Machine Learning, Python, SQL, Spark, Data Science, Software Engineering, Fraud Detection, Anomaly Detection, Distributed Systems, Production Ml
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