Data Science Manager; Tapestry

Position: Data Science Manager (Tapestry)
About Tapestry

Tapestry is Alphabet's moonshot for the electric grid, working at the frontier where energy's complexity meets AI's potential. We were born at X, the innovation lab responsible for breakthrough technologies like Waymo, Verily and Google Brain.

To keep pace with humanity's growing energy needs, the world needs a grid that is visible and understandable. We provide that clarity by building advanced, AI-enabled analytical and planning tools that allow the entire energy ecosystem to plan smarter, move faster, and operate more efficiently-ensuring electricity remains reliable and affordable for everyone.

This is a global effort. Tapestry is proud to support partners in the U.S., U.K., Chile, New Zealand, Australia and Brazil as they build a cleaner, more resilient energy future.

Joining Tapestry allows you to do the best work of your life as part of a multidisciplinary team of experts in AI, energy systems, software engineering and product design-all collaborating to reshape energy on a global scale. If you want to tackle problems that matter and build tools with real impact, we would love to meet you. Learn more about our team and our mission here.

About the role:

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.

How you will contribute to the team…

1. 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:
Persuasively communicate your team's findings and strategic recommendations to senior executives and cross functional teams, tracking the long-term business impact of the solutions.

2. 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.

3. 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.

What you should have…

* 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 delivering production-grade data solutions.

* A proven track record of identifying where data science can add unique value during early product development, alongside a strong ability to influence other teams to collaborate on critical data science work.

* Advanced skills in experimental design, including the ability to architect, guide, and validate robust A/B testing methodologies and statistical experiments in ambiguous environments.

*…