Data Scientist V

TEKsystems•Menlo Park, CA

7d•$80 - $90•Remote

About The Position

This fully remote Senior Data Scientist role will define how specialized research, silicon, engineering lab, and GPU environments are measured. This is a 0-to-1 opportunity to build the goals, metrics, and reporting framework used to evaluate infrastructure usage, capacity, efficiency, and researcher productivity. You will work closely with a Senior data engineer to turn the measurement strategy into scalable data models, dashboards, and reporting. The ideal candidate has a product analytics background, strong SQL and Python skills, and experience creating measurement frameworks from the ground up. This is a product and strategic measurement role; deep machine-learning experience is not required.

Requirements

Senior-level experience as a Product Data Scientist, Product Analyst, or similar analytics professional.
Experience building metrics, goals, and reporting frameworks for a new or previously unmeasured product or platform.
Advanced SQL experience with large-scale data warehouses.
Strong Python skills for analysis and automation using pandas or similar libraries.
Experience defining metric layers, dashboards, and reusable reporting in partnership with data engineering.
Applied statistical knowledge for sound inference and experiment-adjacent reasoning.
Strong stakeholder communication and the ability to influence Senior technical leaders.
Customer-focused mindset with the ability to connect technical measurements to business and user outcomes.
Comfort working independently in an ambiguous environment without an established playbook.
Familiarity with AI-assisted development and analytics tools.

Nice To Haves

Experience with hardware, silicon, GPU or compute services, engineering labs, or research infrastructure.
Experience supporting production engineering, infrastructure, platform, or internal technical products.
Experience with Tableau, Power BI, or a similar dashboarding platform.

Responsibilities

Define success metrics and organizational goals for specialized research, silicon, lab, and GPU environments.
Build a measurement framework from the ground up for areas that do not currently have established metrics.
Analyze usage, demand, capacity constraints, prototyping cycles, and researcher productivity.
Treat internal infrastructure as a product with users, adoption patterns, friction points, and lifecycle stages.
Translate ambiguous leadership questions into clear analytical plans and recommendations.
Partner with a Senior data engineer to develop scalable data models, metric definitions, pipelines, dashboards, and reporting.
Use SQL and Python to work independently from raw data through actionable insights.
Present and defend metric definitions and recommendations to Senior engineering and research stakeholders.
Establish reliable, documented metrics that guide infrastructure planning and investment decisions.