Research Methods Lead

We need to output HTML fragment with sections: About the role? Actually the posting includes sections: About Studyfetch, Why this role exists, What we believe, What you'll own, What we're looking for, Beyond That, The stack you'll work in, What to expect, Compensation & Benefits. We need to use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). However we should only include sections when there is real specific content. The headings we can use: About the role (maybe corresponds to "Why this role exists" or "About Studyfetch"? The instruction: use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can map content to these headings. We need to avoid repeating job title. No Job ID. Remove boilerplate. We need to decide which sections to include: About the role (description of role), Responsibilities (what you'll own), Requirements/Qualifications/Skills (what we're looking for, Beyond That, The stack you'll work in), Benefits (Compensation & Benefits), Pay (maybe part of Compensation & Benefits), Schedule (maybe not present). The posting does not mention schedule specifics, so we should not create Schedule section. Pay: we have salary range, we can include Pay section. Benefits: we have benefits list. Could combine Pay and Benefits? But instruction: prefer few substantive sections over many thin ones. Could have About the role, Responsibilities, Requirements, Benefits, Pay. Or combine Pay into Benefits? But they said use headings for Benefits, Pay, Schedule separately if content exists. We have both Pay and Benefits content, so we can include both. We need to use

• for any list of duties/requirements/benefits. Use

  for prose. We must preserve all substantive information, not invent or summarize away detail. Must not include filler like "commensurate with experience". We need to include all details. Let's extract content: About Studyfetch: description of company, mission, product, Learn Engine, partners, internal validation, need for external evidence. Why this role exists: description of need for external proof, designing studies, owning instruments, building evidence base, founding-team role, working with founders, setting standard. What we believe: mission statements about accessible learning, etc. What you'll own: bullet list of responsibilities: evidence strategy, measurement gap, benchmark, external credibility, research that gets cheaper, ethics and governance, measurement bar before launch blocker. What we're looking for: qualifications: PhD + 7+ years OR fewer credentials with track record building evidence that held up to outside scrutiny. Then "Beyond That": bullet list: built evidence for real audience, rigorous about causality, write and speak clearly, navigated IRB, mission motivation. The stack you'll work in: list of skills: Research design, Measurement, Evidence standards, Analysis, AI/LLM familiarity, Reporting, Prior experience with children's data, publishing/presenting. What to expect: in-person role, fast pace, ownership, autonomy, weeks varied, fit for people who built evidence that changed a decision. Compensation & Benefits: salary range $140k–$170k base, plus equity; 100% employer-paid Medical, Dental, Vision; 75% dependent coverage; 401(k) with employer matching; Daily team dinner provided in-office; small mission-driven team. We need to output HTML fragment only. We'll create sections:

  About the role

  - combine About Studyfetch and Why this role exists and What we believe? Could be about role description. But we might split: About the role (company context), Responsibilities (what you'll own), Requirements (qualifications + beyond that + stack), Benefits, Pay. But we need to preserve all info. Let's decide: Section About the role: includes About Studyfetch, Why this role exists, What we believe. That's a lot of prose; we can put in

  paragraphs. Section Responsibilities: What you'll own (bullet list). Section Requirements: includes What we're looking for (PhD etc), Beyond That (bullet list), The stack you'll work in (bullet list). Could be combined into one Requirements section with multiple

  lists. Section Benefits: list of benefits. Section Pay: salary range + equity. We should not include Schedule. Now ensure we don't repeat headings with empty content. Let's craft HTML. We need to be careful to not include any markdown fences. We'll output:

  About the role

  StudyFetch is the #1 AI-native learning platform globally, transforming how millions of students learn through personalized AI-powered education. We're growing fast with backing from top-tier investors and a mission that's redefining the future of education and ethical learning. 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. We believe every learner deserves the chance to succeed. StudyFetch started with one idea: high-quality, personalized learning should be within reach for anyone, at any stage of life. Honen carries that belief into the workforce. Accessible to everyone. Learning should reach every person, whatever their background, role, or resources. Meet people where they are. Every course adapts to a person's pace, their level, and the way they learn best. Learning never stops. From a first job to a new career, people keep growing at every stage of life. We hire people who share this conviction. The work is demanding and the hours can be long, and what sustains you through it is caring whether a real student finally understands the material.

  Responsibilities

  • Evidence strategy: decide what effectiveness claims to prove, which standard to hold them to (ESSA/WWC-aligned for education, equivalent standards for workforce training), and state plainly when a claim isn't defensible yet.
  • Measurement gap: design outcome measures and study designs — quasi‑experimental, randomized, whatever the question requires — that get us past the ceiling of internal mastery scores and post‑tests.
  • Benchmark: build the rubric, codebook, and interrater reliability process (human and AI raters) behind a tutoring‑effectiveness benchmark meant for external release, reviewed by people with every incentive to find the holes in it.
  • External credibility: recruit and run the independent advisory panel and methods reviewers, lead peer‑review and publication efforts, and represent our research to academic partners, procurement evaluators, and press.
  • Research that gets cheaper every time: build the playbook, reusable instruments, and operating patterns so subsequent studies don’t start from zero.
  • Ethics and governance across populations: handle IRB protocols, human‑subjects determinations, data‑sharing agreements across K‑12, higher‑ed, and enterprise learners, each with different rules and risks.
  • Measurement bar before it becomes a launch blocker: sit close to product and go‑to‑market to say early what evidence a claim will require, instead of finding out after launch that nobody can defend it.

  Requirements

  You're a strong fit if either of these is true:

  • A PhD in Learning Sciences, Educational Psychology, Psychometrics, Applied Statistics, or a related quantitative field, plus 7+ years applying it to real research programs;
  • Fewer credentials on paper and a track record of building evidence that held up to outside scrutiny — ESSA/WWC‑aligned or equivalent, published, reviewed, defended. Show us the work.

  Beyond that:

  • You've built evidence for a real audience, not just an internal one. You can walk us through a study or benchmark you designed, what an external reviewer challenged, and what you'd design differently now.
  • You're rigorous about causality: experimental and quasi‑experimental design, sample and power, and knowing the difference between “this is proven” and “this is our best current guess.”
  • You write and speak clearly. You'll present to engineers, to the founders, and to external academic partners in the same week. You state uncertainty as a number when you can and in plain words when you can't.
  • You've navigated IRB and human‑subjects research before, and you don’t treat it as paperwork — you treat it as part of the design.
  • The mission is why you're here. What sustains you through the hard weeks is the learner on the other end, the one a defensible study proves we actually helped.

  The stack you'll work in (you don't need every item below, but you should be deep in most and able to ramp quickly on the rest):

  • Research design: experimental and quasi‑experimental methods, pre‑registration, sample and power analysis, causal inference
  • Measurement: psychometrics, item response theory, rubric and codebook development, interrater reliability (human and LLM‑assisted raters)
  • Evidence standards: ESSA/WWC tiers or equivalent, IRB protocols, human‑subjects research, data‑sharing agreements
  • Analysis: Python (Pandas, NumPy, SciPy) or R, expert‑level SQL, hypothesis testing at scale
  • AI/LLM familiarity: eval frameworks, LLM‑as‑judge and its failure modes, enough fluency to evaluate whether an AI‑assisted rating pipeline is trustworthy
  • Reporting: technical reports and evidence packages that survive an external reviewer's first read
  • Prior experience with children's data and the governance rules that come with it is a real plus, as is experience publishing or presenting research externally.

  Benefits

  • 100% employer‑paid Medical, Dental, and Vision
  • 75% dependent coverage
  • 401(k) with employer matching
  • Daily team dinner provided in‑office
  • Small, mission‑driven team changing how the world learns

  Pay

  $140,000–$170,000 base salary, plus equity

  We must ensure no extra sections like Schedule. Also ensure we didn't include any boilerplate like EEO etc. We removed. Check that we preserved all substantive info: About Studyfetch, Why role exists, What we believe, responsibilities, qualifications, beyond that, stack, benefits, pay. Yes. We used

  headings as required. Used

  • for lists. Used

    for prose. We didn't repeat job title. No Job ID. We didn't include any filler like "comm