Data Scientist (Genomic Surveillance)

The Opportunity

We are looking for Data Scientists to join the MalariaGEN Data Analysis and Interpretation Team in our Genomic Surveillance endeavour.

The Project and the Organisation

We need effective ways of combating current and future drug and insecticide resistance that will emerge as new malaria control tools are deployed. Genetic data are routinely used in managing viral and bacterial epidemics, and similar approaches are needed to target malaria control interventions for maximum impact and sustainability.

The Malaria Genomic Epidemiology Network was founded in 2005 and has grown to become a data-sharing network with partners in over 40 countries. Our goal is to support and accelerate the use of genomic surveillance data by malaria control programs and policymakers to make effective informed decisions.

As part of the national response, our team is also playing a pivotal role in the Genomic Surveillance of COVID-19, tracking and analysing the spatiotemporal spread of this disease to directly influence and guide policy makers to control the pandemic.

Our Team and the Role

We are a dynamic, diverse, and energetic team of dedicated data scientists with a profound understanding of and passion for biology and epidemiology of infectious disease. We analyse genomic data using cutting-edge methodologies - often developed in-house, interpret the results, and distil them into actionable insights for health organisations.

As a result of investments from the Bill and Melinda Gates Foundation as well as other funders, and after a successful campaign last year, we are continuing to recruit exceptional data scientists to further support our leading genomic surveillance work.

You will contribute to supporting and influencing local and global health organisations in their surveillance activities to achieve well-informed, impactful and sustainable interventions. You will also lead the production of analysis that answer specific, relevant and crucial questions that build upon decades worth of research, as well as the production of open data resource publications to maintain MalariaGEN’s status as a world leader in malaria genomic surveillance.

About you

We are looking for data scientists at the beginning of their career who are keen to work at the cutting edge of genomic research and translation and are eager to develop their analytical skills as part of a dynamic and welcoming team.

We are open to consider a broad range of background skills and experience, with successful candidates having an aptitude in more than one of the following areas:

Undergraduate qualification in computational biology, population genetics, epidemiology or statistics; or undergraduate qualification plus relevant experience in quantitative analysis.

Base computer programming skills for advanced statistical analysis and large-scale data management

Demonstrable knowledge of statistical methodologies

Due to the multidisciplinary nature of the questions tackled, the willingness to effectively integrate with a diverse team of peers and engage positively in scientific discussions is a strong requirement.

Essential Skills
Technical Skills:

Undergraduate qualification or relevant experience in computational biology, population genetics, epidemiology, statistics,
or other relevant area of quantitative analysis
Proficient computer programming skills preferably for the analysis of large scale data
Ability to rapidly gain practical knowledge in new operational environments
Ability to successfully contribute to complex analytical projects involving multiple partners
Ability to deliver high-quality analytical outputs
Knowledge of statistical methodologies
Competencies and Behaviours

Eager to learn new skills
Enthusiastic, flexible and proactive
Proven ability to work independently
Ability to communicate effectively about complex technical matters with both technical experts and non-specialists
Effective written and oral communication and proven ability to work in a collaborative environment
Ability to work effectively with colleagues leading other areas of work within a team environment
Ideal Skills
Technical Skills:

Experience of working in scientific environments
Understanding of and experience with software engineering best practices
Deep understanding infectious diseases biology and epidemiology
Knowledge and experience in population genetics
Successful in producing scientific outputs
Familiarity with genomic and bioinformatics tools and methods
Large-scale computing using on premise and cloud based HPC facilities
Experience with the use of machine learning to answer scientific questions