execution-plan/CAREER-ASSETS.md · UPDATED 2026-09-16
Talent and commercial asset requirements
The objective is a credible scientific-AI profile that a recruiter, hiring manager, founder or R&D leader can evaluate from evidence.
0101
Two hiring narratives to prepare
01
Two hiring narratives to prepare
| Route | Positioning to test against real jobs | Proof to prepare |
|---|---|---|
| Applied AI / scientific data / bioprocess modeling | Hands-on data curation, reproducible models, evaluation and uncertainty in biotech/pharma contexts | FermLit annotation/evaluation, batchlens grouped validation, protein generalization, reproducible papers |
| Scientific AI lead / research engineering lead | Executive and engineering experience translated into scientific delivery, research collaboration and measurable data workflows | Verified leadership stories, architecture tradeoffs, contribution records, measured audit/pilot outcomes, culture fit |
Do not assume CEO experience establishes scientific seniority or that a portfolio substitutes for every degree requirement. Use actual job descriptions to identify roles where the evidence fits.
0202
Required asset milestones
02
Required asset milestones
| Week | Prepare | Completion check |
|---|---|---|
| 00 | Positioning statement, truthful skills/experience baseline, headline/About, 30-second introduction | Every credential/experience claim confirmed or explicitly pending |
| 04 | One quantified technical achievement and release story | Metric links to reproducible result and simulated-data caveat |
| 08 | Technical report narrative and 60-second demo | Problem/method/number/limitation are understandable without the code |
| 13 | Dataset contribution and evidence summary | Annotation quality, provenance, exact N and license scope appear |
| 16 | Publication-led bio and profile refresh | Public status and identifier verified |
| 19 | ATS-readable CV, one-screen portfolio, scientific bio, recruiter message, five application packages | Each role has a current source/date and requirement-to-evidence match |
| 20 | Two recorded ten-minute defenses, CV refresh and application follow-up | Peter can explain results, leakage, assumptions and limits |
| 24 | Collaboration/design-under-constraints story | Contribution and written agreement are real |
| 27 | Distinct recruiter and client calls to action | Hiring narrative remains clear while introducing the audit offer |
| 30 | Audit case-study draft and outcome story | Before/after basis is clear; estimates and client permission separated |
| 32 | Three-paper portfolio refresh and renewed applications | All paper and co-author statuses verified |
| 37 | Role-specific interview/research presentation or pilot plan | Tailored to an actual opportunity and capacity |
| 38 | Talk slides/recording and event proof | Delivery, invitation and publication permission accurately recorded |
| 40 | Final CV/portfolio, career choice and 90-day actions | Evidence supports claims and open obligations have next dates |
0303
Asset contracts
03
Asset contracts
CV: plain Markdown source, concise summary, skills tied to artifacts, verified leadership experience, selected projects/publications with statuses, education as actually held. Prepare PDF/DOCX export during execution if requested/needed. Use no invented performance percentages, titles or degree.
Portfolio: one screen of positioning, two to four strongest artifacts, problem/method/number/limitations, exact publication status and contact. Link deeper proofs. Exclude private target hypotheses and client details.
Recruiter brief: 80–120-word introduction, desired role family, location/remote/work-authorization constraints once known, strongest two proof links and specific next step. Applications use five requirement-to-proof bullets for each verified role, not keyword stuffing.
Interview library: six evidence-backed stories covering scientific rigor, difficult data, failed experiment, collaboration, leadership tradeoff and business outcome. Each states Peter's actual contribution. Add a five-minute project walkthrough and answers on train/test leakage, uncertainty and limits.
Commercial case study: problem, consented context, baseline and observation period, intervention, measured result, uncertainty, limitations and next recommendation. Estimated savings stay labeled estimated. No unnamed client is implied to be real when the example is synthetic.
Culture filter: apply all six questions in v4 section 3.3 before accepting a role/engagement; retain actual answers and Peter's judgment. The source threshold is two bad answers out of six. Do not invent answers on a company's behalf.