Who we are
Congenica is a digital health company developing the gold standard in genomic interpretation platforms for accelerating analysis of rare diseases from genomic data. Simply put, we develop and operate a web-based service to accelerate the interpretation of complex genetic information with the goal of providing life-changing answers for individuals and their families.
Working at Congenica
You will make a real impact on the lives of people around the world with rare diseases. Our products and services enable healthcare professionals to accurately, confidently and rapidly analyse complex genomic data and improve health outcomes.
About the Role
We are looking for a Bio-Statistician to join our quickly growing Innovation team and help to develop and validate complex statistical models, using multivariate data sets to draw targeted conclusions from noisy and non-uniform information. You will be working on a wide variety of clinical datasets, and on data sourced from various sequencing technologies, to generate insight into the genetic disease-causing mechanisms of genetics variants. Working as part of a multidisciplinary team, your analyses will be used by clinical geneticists and biopharma clients worldwide, helping make clinical decisions, provide diagnoses and therapies, and ultimately help find cures and save lives. This role gives you the opportunity to work with petabytes of complex data in the exciting and rapidly expanding area of clinical genetics, as part of a fast scaling and friendly company.
- Design and build statistical models and frameworks to expand the company’s biodata analysis capabilities.
- Participate in the creation and championing of high-quality data analysis algorithms, purposed for the task of engineering and processing large-scale biological data.
- Investigate and curate complex and varied data sets for the purposes of drawing meaningful conclusions regarding targeted project analysis.
- Utilise and develop algorithms to efficiently manage large and heterogeneous data.
- Take part in the knowledge sharing culture at Congenica, training and teaching members of the team as is needed for the project.
- Evaluation of new third-party tools and algorithms to ensure they are of the highest standard.
- Help identify important insights from genetic data and innovative analysis methods.
- Work with our Development team to construct analytical tools and pipelines to exploit new developments at scale.
- Ensure workflows meet business requirements and industry practices.
- As a company we engage in collaborative projects with a small but growing number of academic and commercial partners; analysis and processing of collaborator and customer data will be part of this role.
- Produce presentations, write documentation, and white papers as needed by the company.
Essential attributes candidate must have on entering the role
- Qualification in a statistics related area at postgraduate level or with substantial working experience in this area.
Knowledge, Skills & Abilities
- Bayesian statistical modelling.
- ‘Big data’ statistics and analysis.
- Programming ability in Python and R, with experience using third-party bioinformatics modules and libraries.
- Experience with SQL databases including bulk data entry, query, and schema design.
- Experience in the Unix/Linux environment.
- Complex data visualisation and analysis.
- Working as part of a data analysis team.
- Self-motivated and results-driven, problem-solver.
- Friendly, approachable and builds positive personal and organisational relationships.
- Excellent written and verbal communication.
- Enthusiastic, hardworking, well organised and able to prioritise. Self-driven to learn new skills and knowledge.
- Enthusiastic to share knowledge and skills within the team.
- Professional approach and able to produce high standards of work.
Desirable attributes candidates may have on entering the role
- Qualifications in Biology and/or genetics.
- Dissertation or thesis project involving Bayesian modelling or machine learning.
Knowledge, Skills & Abilities
- Experience with machine learning algorithms, graph theory.
- Experience with maximum likelihood statistical analysis.
- Good understanding of the principles of human genetics.
- Human variation analysis, e.g. population genetics or GWAS, or an area with specific application to variant interpretation.
- Experience with next generation sequencing technology.
- Version control tools e.g. git.
- Knowledge of other languages such as Perl, Java, C/C++.
- Reporting and documentation.
- Awareness of Agile software development principles