Notes from Industry

Across all areas of data science there is huge demand for innovative modeling solutions aimed at forecasting and elucidating dynamic phenomena.

Fig 1. Green, blue = raw data, pink = selected fitted values, black = forecasts from time=23 to 25, grey = 95% Bayesian posterior predictive intervals. Image by author.

High profile use cases of modeling and forecasting dynamic phenomena include:

Machine learning methods, e.g. LSTM networks or random forests, have been used to tackle these and similar…

Challenges and opportunities

The role of data scientist is a relatively new addition to the research and development (R&D) landscape within large life science — pharmaceutical, medical nutrition, biotechnological — companies. For early career data scientists, or established data scientists interested in a move into life sciences research, there are increasing opportunities in this field as it matures and gains recognition alongside established scientific disciplines.

Photo by National Cancer Institute on Unsplash

To be a successful data scientist in a life sciences research environment, particularly in a larger company, requires many of the same skills and experiences as in any other industry. But there are some notable differences:

Fraser Lewis

Senior Team Leader in Data Science R&D at Interests: science, cycling, single malt

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