Technical Intern
Concr
Status: Contractor; Term: Flexible; ~ 10 - 20 hours per week; Accountable to: CTO; Key relationships: CTO, CSO, Scientific Strategy Advisor (SSA)
Purpose
The intern will be a major contributor in applying Concr’s modelling to new datasets. They will work closely with the CTO to expand the range of data used to train and validate Concr’s models.
Specifically, the intern will work to significantly increase the rate of acquisition and ingestion of datasets that Concr can use to train and validate its models.
Successful interns may have the opportunity to publish or speak at conferences about their work. They may also be considered for expanded responsibilities or future roles based on performance and company requirements at the time.
Responsibilities
The key responsibilities for the role include:
- Identifying new datasets that Concr can usefully access.
- Applying for access to datasets when required.
- Cleaning and processing datasets to be compatible with Concr’s pipelines.
- Performing quality control checks on datasets.
- Evaluating the performance of Concr’s models on the new datasets and benchmarking against existing methods and approaches.
- Working with the CTO/COO to streamline the process of ingesting new datasets.
- Working with the CTO to identify improvements to Concr’s internal models.
- Working with the CTO/CSO/SSA to produce materials detailing outputs.
Success measures
Performance in this role will be evaluated based on:
- Ability to work independently: Demonstrates initiative and follow-through with minimal oversight, while proactively seeking clarification where needed.
- Progress against deliverables: Completes agreed tasks (e.g. number of datasets acquired, cleaned, or evaluated) to a high standard and in line with timelines set.
- Quality of analysis and documentation: Maintains a clear and reproducible record of all work, including benchmarking outputs, evaluation results, key model insights, and identified limitations.
- Contribution to team processes: Identifies opportunities to improve or streamline internal workflows related to data ingestion, quality control, or model evaluation.
- Communication and collaboration: Shares findings clearly with relevant team members, contributing to internal discussions, reports, and scientific outputs.
Key requirements
- Programming skills in a common data analysis programming language (ideally Python + Pandas).
- Familiarity with academic literature (ideally in oncology/statistics/data science).
Useful Experience
- Academic research
- Working with confidential data and data access committees
- Development of machine learning/deep-learning models (ideally JAX)
- Bioinformatics analysis of multi-omic data
- Code and data versioning tools (ideally Git + DVC)