Research Methods Lead

StudyFetch•Beverly Hills, CA

5d•$140,000 - $170,000•Onsite

About The Position

We're a technology company building AI-native learning products used by more than seven million students worldwide, alongside Honen, our workforce-learning platform for organizations. Both run on the Learn Engine, the intelligence that moves a learner from initial understanding to demonstrated mastery. We work with partners like NVIDIA to bring responsible, learning-first AI to the students who need it most. We know our platform works — internally. Mastery scores go up, engagement is strong, students come back. What we don't yet have is proof that would satisfy someone with no reason to take our word for it: a district procurement reviewer, an academic peer reviewer, a funder deciding whether to bet on us. That proof has to be built differently than an internal dashboard. It needs outcome measures independent of our own scoring, research designs that hold up to outside scrutiny, and evidence tiered against standards we don't get to define ourselves. That's this role. You'll design the studies, own the instruments, and build the evidence base that lets StudyFetch and Honen make effectiveness claims we can defend to anyone — not just to ourselves. This is a founding-team role. You'll work directly with the people making the decisions, and the standard of proof you set is the one every future study gets held to.

Requirements

PhD in Learning Sciences, Educational Psychology, Psychometrics, Applied Statistics, or a related quantitative field, plus 7+ years applying it to real research programs, OR fewer credentials with a track record of building evidence that held up to outside scrutiny (ESSA/WWC-aligned or equivalent, published, reviewed, defended).
Experience building evidence for a real audience, not just an internal one.
Rigor in causality, including experimental and quasi-experimental design, sample and power analysis, and understanding the difference between proven and best guess.
Clear written and verbal communication skills.
Experience navigating IRB and human-subjects research.
Deep understanding of research design (experimental and quasi-experimental methods, pre-registration, sample and power analysis, causal inference).
Proficiency in measurement (psychometrics, item response theory, rubric and codebook development, interrater reliability).
Familiarity with evidence standards (ESSA/WWC tiers or equivalent, IRB protocols, human-subjects research, data-sharing agreements).
Expertise in analysis using Python (Pandas, NumPy, SciPy) or R, and expert-level SQL.
Familiarity with AI/LLM concepts, including eval frameworks and LLM-as-judge failure modes.
Ability to produce technical reports and evidence packages that withstand external review.
Experience with children's data and its governance rules is a plus.
Experience publishing or presenting research externally is a plus.

Nice To Haves

Prior experience with children's data and the governance rules that come with it.
Experience publishing or presenting research externally.

Responsibilities

Design the evidence strategy, deciding what effectiveness claims are worth demonstrating and to which standards.
Design outcome measures and study designs (quasi-experimental, randomized) to overcome the limitations of internal scoring.
Build a rubric, codebook, and interrater reliability process for an external-release tutoring-effectiveness benchmark.
Recruit and manage an independent advisory panel and methods reviewers.
Lead peer-review and publication efforts, and represent research to academic partners, procurement evaluators, and press.
Develop a playbook, reusable instruments, and operating patterns for future research studies.
Manage ethics and governance across different populations, including IRB protocols, human-subjects determinations, and data-sharing agreements.
Assess evidence requirements early in the product development cycle to prevent launch blockers.