Role: Data Scientist / Machine Learning Engineer – RNA Design
Location:Glasgow, UK (potential for remote)
Start date:March 2025
Period:Full time, permanent
Closing Date for Applications:10th January 2025
Compensation:£45,000-£65,000 p.a. depending on experience
About this job
We are seeking a highly motivated and skilled data scientist to join our dynamic team, focused on leveraging our proprietary platform to develop novel gene control systems for the cell and gene therapy space. As one of the first hires in our growing company, you will have the unique opportunity to help expand and scale our technology platform and help us shape the future of gene therapies.
About Concinnity
Concinnity Genetics is a venture-backed biotechnology company that designs novel control mechanisms for the cell and gene therapy market using our innovative AI platform and synthetic biology expertise. Our platform enables the design, building and screening of large & complex libraries of RNA-based control systems, to allow the precise control of cell and gene therapies in response to a diverse range of molecules. By enabling precise control mechanisms, Concinnity’s unique RNA-based systems will transform the safety of cell and gene therapies.
Key Responsibilities
- Develop and refine AI/ML methods for RNA-based control system development
- Preparation, processing, cleaning, and annotation of datasets tailored for AI development. Manage the curation of these datasets to support various company projects.
- Working within a multidisciplinary team, execute data analysis to a high standard and on schedule, to provide accurate data for seamless transition to subsequent project stages
- Design, test and implement algorithms for structural design space exploration
- Demonstrate strong teamwork and a focus on achieving shared goals with a commitment to high-quality outcomes
Skills
Essential
- AI/ML Development: Proven track record in the successful development and deployment of AI/ML-based tools
- Technical Proficiency: Strong command over major ML frameworks such as Keras, PyTorch, TensorFlow, or Scikit-Learn
- Analytical Thinking: Exceptional analytical skills with a methodical approach to problem-solving
- Collaborative Learning: Eager to acquire new skills and collaborate closely with experimental biology teams
- Biological Insights: Deep interest in leveraging AI/ML for predicting biological structure and function
Desirable
- Cloud Technology: Skilled in using cloud platforms for deploying and managing ML applications
- Predictive Modelling: Experience in building and implementing predictive models specifically for biological applications
- Advanced ML Deployment: Experience in designing and rolling out large-scale machine learning algorithms
- Genomic Data Expertise: Familiarity with handling and analysing Next-Generation Sequencing (NGS) data
- Communication Skills: Ability to communicate complex technical details effectively to both computational and experimental biologists
Qualifications
- PhD/MSc (or equivalent professional experience) in data science/AI, computer science, bioinformatics or other related field
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