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Job description

You will lead the development of the country's Epidemiological Model in support of our clients. You will identify relevant epidemiological data sources, define the country's contextual data layers, lead the data preparation process and help with the creation of the data collectors using our technology stack. You will train the epidemiological models using the Bayesian Engine and a variety of machine learning techniques. Output will be analysed, validated and prepared for publication and in-country usage. You will work in a multidisciplinary team of machine learning and public health professionals. 

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We are looking for candidates that are willing to engage and commit to solving some of the challenges that communities may undergo. We develop solutions for social good and support countries in their fight against diseases like Tuberculosis, HIV and others. Our clients and partners are highly dedicated and work for a cause… We as a company join this fight and innovate to find solutions that make their lives more efficient and their work more effective. 

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Desired skill set

A candidate ideally has knowledge in the following domains or a subset thereof: 

• Programming languages: Python, SQL

• Machine learning: Familiarity with any machine learning techniques such as Bayesian Inference, and data science skills needed to apply modelling techniques to data (data collection, preprocessing). Model validation techniques. Present results in a clear manner. 

• Proficiency in applied statistics including knowledge of statistical tests and how to apply them.

• Familiarity with data visualisation tools.

• Systems (beneficial to have expertise in): PostgreSQL, NIFI

• Test driven development 

• Attention to detail to detail in all aspects of data collection, modelling and reporting is key. 

• Problem solving, independent thinking, and a willingness to learn.

• Familiarity with epidemiological concepts are beneficial but not essential

• EPCON will provide support and training to cover for any skill gaps. ​

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Education level

An ideal candidate has a University degree in one of the following domains: 

• Biomedical engineering

• Computational biology

• Machine learning

• Data scientists

• Computer science

• Epidemiology

• Medicine

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