Principal, Data Scientist

Lead the data science strategy for global deployment and adoption initiatives, driving faster, safer, and more predictable customer onboarding

Architect and deliver advanced analytical, statistical, and machine learning solutions that optimize data migration, configuration validation, risk detection, and adoption outcomes across customer environments

Partner with global stakeholders - including product, engineering, customer success, and implementation teams - to embed data-driven decisioning directly into deployment tooling and workflows

Define success metrics and experimentation frameworks, establishing the leading indicators for customer adoption, time-to-value, and deployment quality across regions and industries

Influence product roadmaps by translating complex data insights into actionable strategic recommendations for senior leadership and stakeholders

12+ years of experience spanning data science, software engineering, and data platform architecture in large-scale, multi-tenant SaaS environments, with a strong foundation in distributed systems and enterprise platforms.

Proven track record of architecting, implementing, and operating data-driven platforms across multiple (3–4+) enterprise products

Hands-on expertise in building and scaling high-throughput data ingestion and processing systems, with demonstrated ability to solve for concurrency, latency, and cost efficiency.

Strong proficiency in at least one core programming language (Python preferred), used for data pipelines, modeling, experimentation, and production ML systems.

Demonstrated ability to operate effectively in a globally distributed team, collaborating across time zones and cultures with product, engineering, and customer-facing stakeholders.

Comfortable navigating high ambiguity, exercising autonomy, and setting technical direction in fast-moving, enterprise environments.

Solid grounding in big data and distributed query technologies (such as Apache Spark, Hive) for large-scale analysis and feature engineering.

Hands-on experience applying AI/ML techniques to observability and operational data, including anomaly detection, root cause analysis, predictive alerting, and system behavior modeling.