Building and Leading the Research Function Questions

Clarify scope & goals
Design a tiered, scalable internal research governance and ethical-review process for ML/AI research that balances safety, scholarly freedom, and speed-to-insight.

Submission requirements

• Short-form intake (automated form, <10 fields): project title, PI, team, abstract (150 words), datasets (sources, sensitivity), compute needs, human-subjects or PII? yes/no, potential harms, mitigation plan, intended outputs (code/paper/product), risk self-assessment.
• Long-form for medium/high risk: methods, training data schema, model cards, evaluation plan, reuse/transfer risks, IRB/partner approvals.

Risk categorization

• Low: public data, no PII, no deployment, purely theoretical/simulations.
• Medium: uses internal or sensitive datasets, potential for bias, models with dual-use concerns.
• High: human-subjects experiments, deployment to users, high-impact models (biological, safety-critical), national-security dual-use.

Review-board composition & cadence

• Standing Research Ethics Board (REBoard): PI-level researcher, ML safety expert, data steward, privacy/officer, legal liaison, representative from affected domain.
• Ad-hoc SMEs added per domain (health, finance).
• Cadence: weekly for triage (low/medium), biweekly deep reviews, immediate ad-hoc for high-risk.

Timelines

• Auto-approve low-risk within 3 business days via checklist and delegated reviewer.
• Medium: decision in 7–10 business days (one full review cycle).
• High: initial decision within 3 business days for hold/fast-track; full review 14 business days; may require iterative mitigation.

Appeal & escalation

• Applicant may request re-review within 5 days with new evidence.
• Escalation to Research Governance Council (senior directors + external advisor) for contested high-risk decisions; final decision within 7 days of escalation.

Avoid blocking low-risk work

• Fast-path automation: checklist, delegated approval to senior researcher pool, template mitigations.
• Pre-approved data/model sandboxes for exploratory work.
• Clear acceptance criteria for low-risk automation; audits post-hoc rather than pre-approval for benign experiments.
• Rolling training: quarterly “office hours” and tooling (automated data-sensitivity scanners, ML-card generators) to reduce friction.

Metrics & continuous improvement

• Track throughput, time-to-decision, appeals, harm incidents, and researcher satisfaction; review quarterly and iterate.

Overview & goals (3 years)
I’d aim to build a research engine that produces 1) publishable novel ML methods, 2) reusable research platforms, and 3) prototypes that inform product strategy.

Yearly milestones (quarterly bullets)
Year 1 — Foundations: Q1 research agenda, baseline datasets, experiments; Q2 prototype core infra (compute, data pipelines); Q3 2 internal technical reports + 1 workshop; Q4 submit 1 conference paper, open-source toolkit.
Year 2 — Scale & Impact: Q1 expand projects to 3 themes; Q2 hire expanded team; Q3 2 conference submissions + 1 industry benchmark; Q4 integrated prototype with product.
Year 3 — Leadership & Ops: Q1 target top-tier publications; Q2 run collaboration grants with universities; Q3 spin-out components to product teams; Q4 measurable product impact & strategic roadmap.

Hiring plan & cadence
Start (3 scientists): month 0.
Months 6–18: hire 1 ML engineer (MLOps), 1 research engineer, 1 applied scientist, 1 PhD intern cohort (2 per year).
Months 18–36: add 2 senior research scientists, 1 research manager, 1 data engineer, more interns.
Focus: balance theorists and engineers (50/50) to ensure publishability + productization.

Tooling & infra by phase
Phase 1 (0–12m): 100–200 GPU-hours/week, experiment tracking (Weights & Biases), Git + CI, storage, dataset catalog. Budget: $400k.
Phase 2 (12–24m): scalable cluster (on-prem or reserved cloud), feature store, reproducible pipelines, labeling tools. Budget: $800k.
Phase 3 (24–36m): productionizable model serving, automated evaluation, grant and travel budget for conferences. Budget: $800k.

Success metrics

• Research: papers submitted/accepted (target 6 subs, 2 accepts by Yr2); open-source releases.
• Engineering: reproducible experiments (100% tracked), prototype latency/accuracy targets.
• Business: prototype adoption (number of product teams using research), estimated MRR impact or cost savings.
• Team: hiring velocity, retention, internship conversions.

Fallback / re-prioritization
If publication targets missed: focus Year 2 on fewer, deeper projects; increase collaborations with academics to boost output.
If infra budget constrained: prioritize shared cloud reserved instances and experiment-scheduling to maximize GPU utilization; defer noncritical tooling.
If hires delayed: use contract researchers, adjunct academic collaborators, and interns to maintain cadence.

I would present quarterly OKRs mapped to these milestones and update priorities every quarter based on publication, prototype, and product feedback.

Overview (role view)
As a research scientist I propose policy guidelines that enable open academic collaboration while protecting strategic IP and company interests.

Pre-publication review flow

• Submit draft + artifact checklist to Research IP Office (RIPO) 30 days before submission.
• RIPO and Tech Lead review within 10 business days for: patentability, export controls, data/privacy, and competitive risk.
• If clearance needs delay, RIPO issues redaction guidelines or negotiates a short embargo (max 60 days) for filing.

Co-authorship & attribution

• Authorship follows disciplinary norms (substantial intellectual contribution + drafting/revision).
• Company-affiliated contributors must declare employment and funding sources.
• External collaborators sign IP/copyright assignment terms during onboarding; authorship disputes escalated to Research Lead for mediation.

Sponsored research & visiting scholars

• Standard templates require joint IP terms: background IP stays with owner; foreground IP either jointly owned or optioned to company with defined license rights.
• Visiting scholars sign NDAs and limited-data-use agreements; publication rights preserved subject to pre-publication review and reasonable redactions.

Open-source release criteria

• OSS release checklist: no sensitive data, no export-controlled models, review for embedded proprietary code, license compatibility (prefer permissive for research libs).
• Release process: internal code review → legal license review → security scan → publish.

Industry-academic conflicts of interest

• Mandatory disclosure of significant financial interests and external appointments.
• Conflict management plans: recuse from evaluations, restricted access to strategic datasets, supervised mentorship for interns with competing affiliations.

Examples & rationale

• For a new model with potential commercialization, we file provisional patents before conference submission; for pure theory we fast-track open publication.
• These rules balance researcher autonomy, academic norms, and protecting strategic assets while keeping timelines predictable.

Hiring Rubric Overview
Score candidates across five pillars (0–5 each): Publication Quality (P), Code & OSS (C), Product Impact (I), Mentorship & Leadership (M), Domain Depth & Vision (D). Weighting for Senior Research Scientist: P 30%, C 15%, I 20%, M 20%, D 15%. For Principal: P 20%, C 10%, I 30%, M 25%, D 15%.

Interview Loop (4–5 stages)

1. Resume + paper/code screen (30–45 min, take-home score)

• Goal: Verify top-tier publications, citation context, OSS repos, deployed projects.
• Sample eval items: venue quality, novelty claim, reproducibility evidence, productionization evidence.
• Scoring: checklist + 0–5 per pillar subset.

2. Technical deep dive (90 min)

• Focus: one core paper or project; walk through math, experiments, failure modes.
• Sample questions: "Show me the derivation of your main theorem and assumptions." "How would you adapt this for real-world constraints?"
• Scoring: P, D primarily; C secondary. Rate clarity, rigor, and scientific judgment (0–5).

3. Code review / reproducibility exercise (take-home or 90 min pair)

• Task: reproduce a result or refactor model for maintainability and scale.
• Evaluate: code quality, tests, documentation, deployment plan.
• Scoring: C and I (0–5).

4. Product & impact interview (60 min with PM/Eng)

• Questions: "Describe how your research influenced product decisions." "Quantify downstream metrics or cost improvements."
• Evaluate: ability to translate research to product, trade-offs, A/B design.
• Scoring: I and D (0–5).

5. Leadership & mentorship (60 min with manager + peers)

• Behavioral prompts using STAR: mentorship examples, hiring experience, running collaborations, setting research agenda.
• Evaluate: mentoring structure, hiring feedback, conflict resolution, diversity efforts.
• Scoring: M (0–5).

Final Calibration

• Compute weighted average. Set thresholds: Senior hire >= 3.6, Strong hire >=4.2. Principal requires demonstrable product impact and leadership — require >=4.0 in I and M and overall >=4.2.
• Document evidence snippets per pillar for panel calibration.

Notes & Trade-offs

• Prioritize reproducible code and product impact higher for industry-facing roles; emphasize publication novelty for frontier-research hires. Include sample scoring rubric template in ATS for consistency.

Overview (approach)
I propose a three-tier portfolio: Blue‑Sky (BS), Foundational Mid‑term (FM), and Near‑term Product (NP). Allocations change by risk profile; governance uses stage gates, quarterly rebalancing with milestone-driven exceptions, and bucket‑specific success metrics.

Allocation (%)

• Conservative: BS 10, FM 40, NP 50
• Balanced: BS 20, FM 40, NP 40
• Aggressive: BS 35, FM 40, NP 25

Rationale: FM stays ~stable (core IP & capability building). NP funds deliver product value and revenue; BS is levered for long-term breakthroughs.

Stage gating & decision rules

• Gate 0 (Idea): short feasibility check (3–6 wks). Pass rate target ~30%.
• Gate 1 (Prototype): technical proof-of-concept + baseline experiments (3–6 months). Require reproducible results and initial simulation/benchmarks.
• Gate 2 (Validation): robustness, scalability, ablation studies, draft paper or internal tech brief (6–12 months). Business/PM evaluates product fit for NP migration.
• Gate 3 (Scale/Transfer): production readiness or open research release. Funding scales up/down per gate outcomes.

Decision criteria use pre-registered hypotheses, evaluation on held-out tasks, compute/resource budgets, and publication/competitor landscape.

Rebalancing rules

• Quarterly portfolio review to shift up to ±10% between buckets based on milestone outcomes and strategic priorities.
• Allow ad‑hoc reallocation when a BS project reaches Gate 2 (promote into FM/NP) or NP underperforms 2 consecutive quarters (funds diverted).

Success metrics (per bucket)

• BS: novelty and frontier impact — number of novel methods with strong empirical/ theoretical signals, publications at top venues, citations/attention, probability of IP spinouts. Measure via validated breakthroughs per 5 researcher-years and qualitative peer reviews.
• FM: technology readiness & tooling — robust algorithms, open‑source libs, reproducible benchmarks, patents; metric: time-to-adoption by internal teams, reduction in downstream R&D cost, accepted papers + internal deployments.
• NP: business impact — feature launches, user engagement uplift, revenue/efficiency gains, latency/accuracy improvements in production; metric: ROI, A/B lift, adoption rate.

Example application
A balanced profile: start BS on two high-risk ML theory topics (20%), maintain FM on model robustness toolchain (40%), NP on product personalization (40%). Quarterly, if BS proof shows strong generalization and theoretical promise, promote 10% to FM and seed experiments for productization.

Trade-offs & governance
Maintain a research council (senior researchers + PMs) to adjudicate subjective cases; ensure compute accounting and reproducibility standards to reduce optimistic carryover.