Description review
Elman (Deep Science Ventures) | Co-founder & CTO | London, UK-based preferred / REMOTE
Elman (Deep Science Ventures) · London, UK-based preferred / REMOTE · back to the listing
HR standards
49/100
poor
Title ↔ description
52/100
needs work
Reads as
Machine Learning Engineer
96% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
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What the listing never says
- No section describes what the person would actually do. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
considered| Full-time | Pre-seed
We're building AI to predict clinical trial and drug development outcomes, and quantify how much confidence those predictions deserve. By linking the evidence available at a decision to the judgement made and what happened next, we aim to learn which experimental findings predict outcomes in which therapeutic settings. The platform is already running, with work from an Allen Institute collaboration described in this preprint https://elman.ai/preprint.
We want a hands-on technical co-founder to lead development, build the evaluation programme and hire the first technical team once funded. Benchmark design, applied statistics and production LLM systems. The modelling approach is open. Biology experience useful, not required.
Founder equity.
Role and application: https://elman.ai/hn
We're building AI to predict clinical trial and drug development outcomes, and quantify how much confidence those predictions deserve. By linking the evidence available at a decision to the judgement made and what happened next, we aim to learn which experimental findings predict outcomes in which therapeutic settings. The platform is already running, with work from an Allen Institute collaboration described in this preprint https://elman.ai/preprint.
We want a hands-on technical co-founder to lead development, build the evaluation programme and hire the first technical team once funded. Benchmark design, applied statistics and production LLM systems. The modelling approach is open. Biology experience useful, not required.
Founder equity.
Role and application: https://elman.ai/hn