Interview scorecard template

Longevity Escape Velocity Strategist interview scorecard

Evaluate Longevity Escape Velocity Strategist candidates across 4 weighted areas: theoretical command, from theory to hardware or code, research judgement, and explaining it to non-specialists. Theoretical command leads at 35%, so check command of the hallmarks of aging framework, epigenetic clocks (Horvath, GrimAge, DunedinPACE), Gompertz mortality modelling, and where senolytic or partial. Use the rubric to compare role-specific evidence consistently.

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frontier research deep techaging biomarkersbiotech roadmappinggerosciencesenolytics
TL;DR
For theoretical command, look for evidence the candidate cites specific intervention classes and clock validation limits, distinguishes lifespan from healthspan data, and names where the mouse-to-human gap breaks. For from theory to hardware or code, look for evidence the candidate shows a concrete artefact, for example a modelled LEV timeline, a biotech landscape map, or a funding thesis that shaped real allocation decisions. Apply the written 1–5 anchors to every answer, record the evidence behind each rating, and use the factor weights to reach a consistent overall assessment.
Complete evaluation framework

What to assess and how to score it

Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.

01
Evaluation factor

Theoretical command

35% weight

Check command of the hallmarks of aging framework, epigenetic clocks (Horvath, GrimAge, DunedinPACE), Gompertz mortality modelling, and where senolytic or partial reprogramming evidence actually stands.

Evidence to listen for

  • Explains the underlying theory at the level the role demands, and can go a layer deeper when pushed
  • Knows which results are established and which are contested
  • Distinguishes their own contribution from the field's
  • Comfortable saying where the theory runs out

Five-point scoring guide

1
Poor

Recites terminology without understanding; cannot go one layer deeper.

2
Needs Improvement

Surface familiarity; conflates established results with speculation.

3
Satisfactory

Solid grasp of the core theory; thin at the frontier.

4
Very Good

Strong command; separates settled results from open questions.

5
Excellent

Cites specific intervention classes and clock validation limits, distinguishes lifespan from healthspan data, and names where the mouse-to-human gap breaks.

02
Evaluation factor

From theory to hardware or code

30% weight

Probe translation work: intervention roadmaps, ITP or Dog Aging Project data they modelled, trial design input, portfolio theses, or forecasting code they built in Python or R.

Evidence to listen for

  • Has built, simulated, or run something real, not only published about it
  • Knows the gap between the idealised model and the actual apparatus or system
  • Names the practical constraint that dominates in real conditions
  • Can describe a result that did not match prediction

Five-point scoring guide

1
Poor

Purely theoretical; no contact with implementation.

2
Needs Improvement

Some exposure but unaware of practical constraints.

3
Satisfactory

Has implemented work; understands the main real-world limits.

4
Very Good

Strong practical record; articulate about theory-versus-reality gaps.

5
Excellent

Shows a concrete artefact, for example a modelled LEV timeline, a biotech landscape map, or a funding thesis that shaped real allocation decisions.

03
Evaluation factor

Research judgement

20% weight

Assess how they weigh hype against evidence: dismissing or backing rapamycin, plasma dilution, yamanaka factor reprogramming, and how they decide which bets deserve capital or attention.

Evidence to listen for

  • Chooses problems by tractability and value, not novelty alone
  • Knows when to abandon a line of work
  • Reads and evaluates others' results critically
  • Can say what would falsify their own approach

Five-point scoring guide

1
Poor

Chases novelty; no sense of tractability or when to stop.

2
Needs Improvement

Weak problem selection; persists past the point of value.

3
Satisfactory

Reasonable judgement within a defined programme.

4
Very Good

Selects problems well and knows when to abandon a line.

5
Excellent

Names claims they revised after new data, separates mechanism plausibility from clinical evidence, and states falsifiable conditions for their own forecasts.

04
Evaluation factor

Explaining it to non-specialists

15% weight

Test how they brief funders, regulators, or press without overselling immortality: explaining actuarial escape velocity, biomarker surrogacy, or FDA aging-indication hurdles to non-biologists.

Evidence to listen for

  • Explains the work to an engineer, an executive, or a funder without either mystifying or dumbing it down
  • Writes clearly
  • Collaborates across disciplines
  • Makes the case for resources in terms the audience cares about

Five-point scoring guide

1
Poor

Cannot communicate outside their specialism.

2
Needs Improvement

Explanation is either impenetrable or hollow.

3
Satisfactory

Adequate with technical peers; less effective with lay audiences.

4
Very Good

Explains clearly to specialists and non-specialists alike.

5
Excellent

Translates escape velocity maths into plain scenarios with explicit uncertainty, and resists the sensational framing journalists and investors push for.

Evidence-led prompts

Interview questions for a Longevity Escape Velocity Strategist

Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.

  1. 01

    Can you describe projects you have been involved in relating to ageing research?

  2. 02

    What experience do you have with technologies aimed at extending healthy lifespan?

  3. 03

    Can you discuss partnerships or collaborations with research institutions?

  4. 04

    How do you measure the effectiveness of clinical interventions in this area?

  5. 05

    Can you describe your experience with data analysis in biological research?

See the complete Longevity Escape Velocity Strategist question set
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