Data Scientist IV

This fully remote Data Scientist role focuses on infrastructure and operations analytics across multiple technical domains. You will serve as the primary analytics partner for engineering and platform teams, building data products, dashboards, and reporting frameworks that make complex infrastructure data usable and reliable. The work is broad, hands-on, and customer-facing, with a strong emphasis on practical SQL and Python, operational statistics, and close collaboration with technical stakeholders to improve visibility, decision-making, and reporting hygiene in areas that have had little or no data science coverage.

• Own day-to-day analytics for horizontal infrastructure operations, including intake, scoping, and turning around analysis requests from partner teams.

• Work end-to-end from raw data to finished, trustworthy answers using SQL and Python, ensuring outputs are clear, accurate, and actionable.

• Build, ship, and maintain dashboards and self-serve reporting so recurring questions become self-service instead of one-off requests.

• Design reporting that engineering partners actually adopt, iterating based on feedback to maximize usability and impact.

• Support operational areas such as asset inventory, network-side systems, and client platform and endpoint services with robust analytics and reporting.

• Provide the analytical layer for infrastructure data products and services, including shared interfaces and tooling that make infrastructure data usable across the organization.

• Establish baseline metrics, data quality checks, and reporting hygiene in domains that previously had no dedicated data science support.

• Partner closely with data engineers on data models and pipelines, flagging gaps, shaping schema design, and influencing what gets built to support analytics needs.

• Manage an inbound request queue from technical partners, clarify vague or ambiguous asks, set expectations, and close the loop with clear communication and documentation.

• Apply working statistical knowledge to operational analytics, including descriptive statistics, trend and anomaly detection, and sound sampling and significance judgment.

• Operate comfortably in AI-assisted development environments, using AI tooling as part of the daily analytics workflow for analysis, data wrangling, and light automation.

• Collaborate with data engineers, data scientists, and infrastructure and platform engineering teams who consume infrastructure data products and rely on reliable, well-documented data.

• Provide operational visibility and trusted reporting to team leadership to support informed decision-making and oversight of infrastructure and platform performance.

• Adapt quickly to unfamiliar domains and projects without a fully-formed playbook, bringing generalist range to support diverse infrastructure operations areas.

• Maintain clear documentation of data sources, dashboards, metrics definitions, and analytical methodologies to ensure transparency and reusability across the organization.

• Solid, practical SQL used daily, with proficiency working independently from raw data to a finished answer.

• Experience writing performance-aware SQL against large-scale data warehouses, including joins and window functions.

• Strong Python skills for analysis, data wrangling, and light automation, using libraries such as pandas or equivalent.

• Hands-on experience designing, building, shipping, and maintaining dashboards and self-serve reporting that partners adopt and use regularly.

• Working statistical knowledge for operational analytics, including descriptive statistics, trend and anomaly reasoning, and sound sampling and significance judgment.

• Demonstrated…