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. MADE.com), 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).