Member of Technical Staff (Data)
TLDR
Own analytical areas end-to-end, working with large datasets to provide structured insights and answers for sustainability researchers and institutional investors.
At least 2 years of professional experience in data analysis, data science, research, consulting, or a closely related analytical field.
Strong analytical and quantitative thinking. You reach for the right tool (a pivot table, a SQL query, a notebook, a Bayesian model, an LLM, a back-of-the-envelope sanity check) instead of defaulting to one.
Stubborn about correctness: you notice when a number looks slightly off, and you keep pulling on the thread until you understand why.
Comfortable working with large, messy datasets, SQL fluency is a must, Python (pandas / notebooks) or similar is a strong plus.
Comfortable collaborating closely with non-engineering domain experts (sustainability researchers, analysts, customers) and turning expert judgment into structured, defensible analyses.
Comfortable working with LLM-assisted features, using AI tools to speed up your own analysis, evaluating LLM-generated outputs, designing checks that catch model regressions, even if you don't consider yourself an ML or coding specialist. Curiosity about agent-assisted workflows matters more than prior experience with them.
Strong output orientation and common sense thinking to enable solving hard-to-define problems.
Ability to communicate clearly both verbally and in writing, especially turning a complicated analysis into a clean explanation a non-expert can act on.
Solid track record of internal passion for excellence: you have gotten things done clearly better than what was required, because you enjoy doing things well.
Experience working with data in a regulated, expert-driven, or otherwise domain-heavy area (finance, sustainability, climate, healthcare, public policy, scientific research, etc.).
Hands-on experience designing checks, evals, or QA processes that catch issues in large datasets or model outputs.
Comfort with applied statistics, predictive modeling, geospatial analysis, or other quantitative methods.
Experience contributing to data pipelines, not necessarily owning the infrastructure, but understanding how the data gets to you and being able to suggest fixes upstream.
Familiarity with cloud / data-warehouse tooling (BigQuery, Snowflake, AWS, dbt, or similar).
A chance to join a quickly growing and highly ambitious impact SaaS company with a mission that matters — real capital allocation decisions at 1,000+ institutional investors and corporations rest on the data we build.
A team of exceptional people who are kind, direct, and care deeply about doing the work well.
An unusually AI-forward environment — first-class tooling, in-house agents, and the freedom to keep pushing what "AI-native development" actually means in practice. You'll be shaping the workflow, not inheriting it.
Substantial autonomy and ownership from day one, with lots of room to grow.
Competitive compensation, including stock options and a comprehensive healthcare package.
The Upright Project is an impact SaaS company that empowers organizations to make scientifically-backed decisions regarding their net impact on society and the environment. By leveraging a proprietary data model derived from over 200 million scientific articles, we enable over 1,000 institutional investors and corporations in North America and Europe to measure and optimize their sustainability efforts amidst a growing demand for transparency and accountability.