Commons is an award-winning app that helps people make sustainable spending choices. With the app, consumers discover climate-friendly brands and earn rewards on eligible purchases. Commons is backed by Sequoia Capital, Jay-Z, and the founders of Headspace, Fitbit, and Nest, and has been selected as a TIME100 Most Influential Company of the Year, Apple App of the Day, and a Fortune Impact 20 company.
The opportunityWe’re looking for an ambitious Data/AI Engineer to help people make purchasing decisions that are better for them and the planet.
Reporting to our Head of Data, you'll work at the intersection of data engineering, AI, and climate action—creating the tools that power our ability to drive transparency and accountability in consumer spending. You'll build intelligent systems that gather, analyze, and scale data to evaluate purchases and brands on sustainability—turning complex environmental data into clear, actionable insights that inform real people’s choices in the real world.
Who you are:You're a pragmatic problem-solver who finds creative ways around obstacles. You excel at building scalable, data-driven solutions to ambiguous challenges. Whether you're writing SQL queries or researching harmful ingredients in supply chains, you approach problems with curiosity and rigor. You move fast, learn continuously, and believe the climate crisis demands
Build scalable data infrastructure. Design and maintain pipelines that source, clean, and process large datasets, enabling us to evaluate thousands of brands efficiently.
Unlock insights through rapid analysis. Conduct targeted analyses that answer critical business questions and help users make informed purchasing decisions.
Apply AI to real-world problems. Develop agentic tools and leverage LLMs to automate data collection, improve recommendation systems, and scale our ability to assess brand sustainability.
Collaborate across teams. Partner closely with Engineering and Product to understand our systems deeply, identify improvement opportunities, and scale solutions as we grow.
Learn and iterate relentlessly. Embrace experimentation, treat learning as the primary outcome of your work, and push the team to move faster while staying focused on impact.
Measure what matters. Define metrics to track progress, demonstrate system improvements, and quantify the real-world impact of your work.
Technical foundation. Proficiency in SQL, Python, and building data pipelines, with at least a few years of experience applying this in fast-moving work environments. It’s a plus if you have experience with scraping and cleaning data.
AI-forward mindset. You're an early adopter of GenAI tools and actively explore how LLMs can transform workflows. Bonus: experience applying AI to large-scale data collection, analysis, recommendations, or consumer applications.
Structured thinking. You approach problems methodically—seeking out new data sources, validating hypotheses, and building systems that scale. You're skeptical in the right ways and data-driven in your decision-making.
Bias for action. You thrive in fast-paced, ambiguous environments. When blocked, you find another path. You ship valuable work quickly and aren't afraid to iterate.
Clear communication. You gather requirements efficiently, synthesize information into clear next steps, and communicate proactively in writing and conversation.
Passion for sustainability. You're motivated by helping people make informed choices and holding companies accountable. You're familiar with—or eager to decode—corporate sustainability practices. You’re a discerning customer yourself and skeptical of false claims.
Think you might have what it takes, but not sure you meet every requirement? Research shows that women and marginalized folks tend to only apply when they check every box. If you're passionate about what we're building, please apply. You might be just who we’re looking for.
.png)
