About us
Serna Bio is pioneering the first map of the druggable transcriptome, unlocking the vast potential of RNA biology for small-molecule drug discovery. Our innovative Discovery Platform addresses traditionally challenging targets across both coding and non-coding RNA.
We are seeking a skilled High Throughput Screening Data Scientist to enhance our analytics capabilities and help drive our research initiatives forward. You will be a key member of the computational biology team, working closely and cross-functionally with our biology and engineering teams, primarily focussed on organising data associated with HTS screening data
The Impact You’ll Make
- Lead and contribute to the design and execution of bioinformatics pipelines for the analysis of small molecule HTS data
Requirements
About you
Technical Requirements:
- MSC/PhD or equivalent in statistics, biostatistics, epidemiology, or similar
- Experience with data analysis from HTS small molecule screens and pooled CRISPR Screens
- Experience in ensuring the right data is integrated into assay design to support robust statistical testing approaches by working with the drug discovery team
- Proven experience working in small, interdisciplinary teams, remote teams and async communication
- Previous experience in working with biology teams for experimental analysis in support of drug discovery programs in a commercial environment
- Expert in Bioinformatics programming and scripting languages (Python, Perl, R).
- Bonus: Experience working with AWS/GCP and good coding practices, especially writing code in a scalable manner
Interpersonal Requirements:
- Exceptionally detail-oriented with a love for organisation
- Excellent communication skills - able to present findings whilst working collaboratively within the organisation
- Comfortable with juggling activities and can (re) prioritise effectively
- Previous experience in a start up or a clear rationale to join a small company
Benefits
About Us
Serna Bio is a preclinical drug discovery company developing small molecules that can modulate translation and splicing, by targeting RNA. Founded in 2021, Serna Bio leverages the convergence of synthetic biology, machine learning (ML), and massively multiplex screening to tackle the historical challenges of drugging the RNA universe.
Our core innovation lies in the Serna Discovery Platform, which systematically maps RNA structures to a vast chemical universe, generating large proprietary datasets. The platform incorporates both experimental wet lab and AI capabilities. We use our platform to tackle undruggable targets and novel biology inaccessible by classical drug discovery.
The Values You’ll Share
- The Data Decides: The future of drug discovery will be computationally led. Opinions are just hypotheses. The data rules.
- Unapologetic ambition: The work we do matters. We maximise its value by striving to make the biggest impact, fastest. We don’t take the easy path, or wait for lower risk. We show up with a plan. We define what victory looks like. We make our time count.
- We don’t rely on experience, we run the experiment: We are humble in the face of work that has never been done before. We never default to experience. We use what we already know to train the machines to outperform us.
- Only the highest standards: We are inspired by challenge. We work hard. Our efforts are guided by the utility of every failed experiment and the value of new learning. Metrics matter to us. Effort, integrity, honesty, execution, and excellence are what we owe each other.
- We are one company: We operate with a ‘company first, team second’ mentality. We learn from the perspectives of others. Our is an antedisciplinary idea meritocracy. Ego is valueless. Diverse perspectives, integrity, resilience and teamwork are priceless.
- High-density, high-frequency communication: We share Work in Progress. We offer and accept critical opinions on this work so that the final versions are excellent. We communicate clearly to enable rapid decision-making, and we bring solutions, not just problems to the team.