Are you a sharp, data professional who likes making messy real-world data make sense? We're looking for an experienced data professional to join our technical staff and help build the next generation of our impact data products by asking the hard questions of large datasets and making sure the answers we ship actually hold up.

At Upright, you get to work on a product that actually matters: the world's largest open-access database on company impact, used by 1,000+ institutional investors and corporations to make real capital allocation decisions. We quantify companies' impact from the ground up, based on peer-reviewed science and what companies actually produce and sell. The raw material is their product data, which we collect and classify automatically at scale.

A few things our team has built recently:

• A global physical climate-risk model for any company. We built a pipeline that pulls in open scientific datasets on heat stress, wildfires, and coastal and riverine flooding, projects them onto a worldwide grid of ~145 million points under both moderate and high-emissions scenarios, and turns the raw data into per-location risk scores. Customers can now see, for any company they hold or are considering, how exposed its real-world locations are to physical climate hazards today and out to 2050.

• An LLM-assisted Double Materiality Assessment engine that automates a process consultants currently charge €50–200k for, with structured outputs, retrieval over peer-reviewed evidence, and a custom eval harness keeping quality measurable.

• An agentic user interface, through which users can ask any freeform questions about Uprights data and beyond. And all the quality guardrails to ensure the data is as accurate, coherent, and well-positioned as in our traditional UI.

As a Member of Technical Staff focused on data analysis, you'll own larger analytical areas end-to-end: defining what "good" looks like for a given dataset, digging into where the current numbers fall short, designing the checks and analyses that catch issues, and working with our engineers and domain experts to fix them at the root. You'll work closely with our sustainability researchers, analysts, and customers to turn fuzzy, expert-led questions into structured, defensible answers. Your specific responsibilities will be tailored during the recruitment process to your background, skill level, and interests.

SIGNS FOR BEING A GREAT MATCH:

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.

ADDITIONALLY, WE VALUE:

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).

WHAT WE OFFER:

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.