Data Scientist

Fora•New York, NY

4h•$158,000 - $239,000•Onsite

About The Position

Fora is hiring a Data Scientist to serve as the analytical partner for an area of the business and own its most challenging questions: whether initiatives are working, why, and what to do next. The work is largely causal and includes building data products, reusable tools, and AI workflows used across the company. Data Science is a central organization that supports decision-making across product, marketing, and company leadership. The team owns the metrics Fora uses, experiments that establish what works, and analyses that inform the roadmap. Focus areas include product outcomes such as bookings, growth from acquisition through retention, experimentation and causal methods, and evaluation of AI agents. The role is based in Fora’s New York City office in Tribeca/Fidi. Compensation varies by experience and hiring level, with an indicative range of $158,000–$239,000 plus equity. Authorization to work in the United States is required for full-time roles based in the NYC office; Fora cannot sponsor or assist with U.S. work authorization.

Requirements

Experience taking ambiguous questions through to decisions and measurable results.
Ability to write production SQL and Python and verify the accuracy of analyses.
Ability to explain technical work plainly to executives and influence decisions with evidence.
Experience running experiments end to end and using causal inference when experimentation is not possible.
Experience using AI tools to work faster and checking their outputs.
Authorization to work in the United States for full-time roles based in the New York City office; Fora is unable to sponsor or assist with U.S. work authorization.

Nice To Haves

Experience with R.

Responsibilities

Measure the effects of changes using experiments, holdouts, and quasi-experimental methods.
Define success metrics, including north-star, leading, and guardrail metrics, from event logging through metric definition.
Turn analytical findings into decisions and follow up to determine whether those decisions worked.
Partner with product and engineering to understand what drives bookings rather than usage.
Analyze growth from acquisition through retention, including incrementality, churn, and early indicators of success.
Design experiments and apply statistical and quasi-experimental methods.
Evaluate AI agents using evaluation sets, LLM judges, and links to bookings.
Build data products, reusable tools, and AI workflows used across Fora.