Design and implement scalable data models, domain contracts, and schemas with strong guarantees on performance, integrity, lineage, and governance.
Build and optimize batch and streaming pipelines (ETL/ELT, near-real-time) with clear SLAs on latency, quality, and cost.
Drive platform reliability through observability primitives including SLIs/SLOs, freshness and completeness checks, lineage tracking, and automated parity tests.
Develop, validate, and deploy statistical and ML models for security use cases such as anomaly detection, behavioral modeling, and risk scoring.
Productionize models as reliable services with well-defined APIs, feature stores, versioning, and continuous monitoring for drift, bias, and performance.
Translate large-scale security telemetry into actionable risk intelligence and automated decisions.
Design and deliver agentic workflows that combine perception, reasoning, and action to reduce time-to-detection and time-to-mitigation.
Integrate LLMs with security pipelines to automate root-cause analysis, contextual explanations, investigation summaries, and response orchestration.
Expose read-only and action APIs for downstream systems and dashboards (e.g., executive, SOC, and customer-facing views).

Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Engineering, or equivalent practical experience.
8+ years of experience building and operating large-scale data or software systems with high throughput and low latency.
Strong proficiency in Python (preferred), Scala, or Java, with excellent software engineering fundamentals.
Expertise with data and stream processing technologies such as Airflow, Spark, Kafka, Flink, or equivalents.
Solid SQL skills and experience with at least one NoSQL or distributed data store.
Practical experience deploying and operating ML systems in production, including monitoring and lifecycle management.
Cloud experience with AWS, GCP, or Azure and managed data/ML services.
Strong understanding of statistics and machine learning methods and their real-world tradeoffs.
Excellent communication skills, with the ability to explain complex technical concepts to diverse stakeholders.
Working knowledge of data privacy, secure data handling, and regulatory requirements (e.g., GDPR, CCPA).

Lead Member of Technical Staff – Security Data Science, ML Engineering

Key skills