Data Science Manager, Tapestry

About Tapestry

Tapestry is a team within Alphabet working to build the AI-powered electric grid. We are tackling one of the world’s most important infrastructure challenges: helping the energy system become more visible, understandable, reliable, affordable, abundant, and clean.

Originally born at X, Alphabet’s moonshot factory, Tapestry brings together experts in energy, AI, software, engineering, and product to build tools that help the electricity ecosystem plan smarter, move faster, and operate more efficiently.

This is a global effort. Tapestry supports partners across the U.S., U.K., Chile, New Zealand, Australia, and Brazil as they work toward a cleaner, more resilient energy future.

Joining Tapestry means doing high‑impact work with a multidisciplinary team tackling a problem that matters at global scale.

At Tapestry, data drives all our decision-making. Data Scientists work across the organization to help shape our business and technical strategies by processing, analyzing, and interpreting massive datasets. They lead our metrics assessment, analyze massive datasets and derive early insights, and partner with cross‑functional teams on the right datasets for maximum downstream impact.

As a Data Science Manager, you will act as a pivotal technical leader to bridge the gap between complex business questions and advanced technical execution. You will build, mentor, and lead a high‑performing team of data scientists to deliver operational excellence, accelerate product advancement, and drive business value.

In this role, you will deeply immerse yourself with the team of data scientists in data collection and analysis, develop compelling, synthesized recommendations for senior leadership, and be involved to help drive implementation. Ultimately, your team's solutions will fundamentally improve electric grid visibility and resilience.

• Team Leadership and Strategic Delivery
• People Management:
  Recruit, mentor, and lead a world‑class team of data scientists. Cultivate talent through active technical mentorship and clear career development paths.
• Cross‑Functional Alignment:
  Collaborate with engineering, product, power system experts, and external partners to translate high‑level business goals into rigorous data science roadmaps.
• Executive Communication:
  Persuadefully communicate your team's findings and strategic recommendations to senior executives and cross functional teams, tracking the long‑term business impact of the solutions.
• Data Integrity and Curation Strategy at Scale
• Pipeline Oversight:
  Guide the team in discovering, investigating, and deriving insights from large and complex input datasets, both current and potential, from partners and other sources.
• Gatekeeping Metrics:
  Oversee the definition of problem framing, test datasets, and core business, product and performance metrics that machine learning models will aim to optimize for.
• Multi‑Stage Quality Control:
  Ensure data integrity across the pipeline by establishing frameworks to assess intermediate datasets and metrics within multi‑stage machine learning processes.
• Annotation Rigor:
  Drive a comprehensive and scalable data annotation strategy that prioritizes quality through statistical rigor, ensuring data reliability for all downstream modeling.
• Problem Definition and Advanced Analytics
• Grid Visibility and Innovation:
  Lead the proactive exploration of new problem spaces to fundamentally improve electric grid visibility and resilience.
• Experimentation Frameworks:
  Standardize how the team designs, executes, and analyzes A/B tests and other experiments to validate hypotheses and measure product impact.
• Engineering Best Practices:
  Champion modern data science workflows, including the application of GenAI techniques for data analysis, ensuring the team follows robust engineering best practices.

• PhD or Master’s in a quantitative field and 8+ years of tech or energy industry work experience as a statistician, quantitative analyst, or data scientist.
• 5+ years of experience directly managing or leading high‑performing data science and analytics teams, with a proven track record of…