Science to Data Science Fellowship

August & September 2018

PIVIGO, London – UK

Pivigo's Science to Data Science (S2DS) Fellowship program is one of the most competitive data science fellowship programs in the world, and Europe’s largest. Only 90 fellowships are awarded each year.


About the Fellowship

The Pivigo Data Science Fellowship program is of a similar format and calibre to the US-based Insight Data Science Fellows program, and the London-based ASI Fellowship. Data science fellows have highly analytical, data-intensive, research backgrounds, with the majority having PhDs in neuroscience, computational chemistry, mathematics, astrophysics, physics, computational biology, bioinformatics, environmental science, or computational linguistics.

Fellows range from newly minted PhDs, early- and mid- career postdocs, senior researchers – in some cases tenure-track professors, and ex-academics who have already made the leap into industry. The majority of the fellowship is spent working on commercial data science problems; either as external consultants or embedded in establish data science teams, and in tech startups (e.g. Shoppar), established or high-growth technology companies (e.g., data science consultancies (e.g. HAL24K), small and medium sized enterprises (SMEs), multi-national companies, or charities and non-government organisations. Fellows are supported by commercial experts – e.g. CTOs or Heads of Engineering/Analytics, technical mentors – experienced data scientists and technologists from industry, and work as part of a data science team to develop high-impact, cutting-edge, sustainable, and scalable data science solutions. 

A core component of the fellowship is an intensive MBA-like program, focussing on commercial and leadership aspects of data science. Topics include data science in business, introductory economics & finance, business strategy, commercial insight, product management, management consulting, and ethics in data science. Technical lectures focus on best practises in software development and machine learning algorithms, Python & R, effective data visualisation and communication, and commonly used enterprise platforms and tools (e.g. Microsoft Azure).


Experience & Outcomes