Data Science in the Biotech/Pharma Research Organization

Abstract: 

In this hands-off tutorial, I will provide a framework for thinking about, and hence organizing, data science in biotech and pharmaceutical research organizations. Together, we will cover: (1) what the core mission of a data science team should be, (2) the ways a data science team can deliver value to the research organization, (3) major classes of problems and methods, and (4) challenges that are unique to a data science organization in the _research_ space, as contrasted to clinical development, manufacturing, and commercial organizations. By the end of this session, data science leaders at biotech and pharma companies who attend this session will be equipped with frameworks for for thinking about data science problems in the biotech and pharma research space. Executives who are unfamiliar with the research space of data science problems will walk away with a broad, high-level overview of data science problems in research and how to frame and understand their value.

Session Outline:

(1) The core mission of a data science team is two-fold: accelerating the pace of science, and enriching the quantitative suite of insights from existing and new data. We will explore this topic in moderate depth to anchor the rest of the tutorial.

(2) Data science teams deliver value to a research organization by automating the analyses that humans would otherwise do, unlocking quantitative capabilities not previously available to scientists, providing ways to shortcut the amount of experimentation necessary to achieve a target hit. We will explore examples together, both from my own experience, and crowdsourced from the audience through an opened-up discussion.

(3) The major classes of problems that data science can impact in the discovery research space cover the holy trinity of drug discovery: (molecule, target, indication) triplets. However, we will also explore alternative angles: framing problems as a matrix between biological modality (mRNA, small molecule, protein, cellular therapeutics, microbial ensembles) and quantitative methodology (e.g. AI library designs, computer vision, probabilistic modelling, custom algorithms, etc.). Time permitting, through an open floor discussion grounded in some examples from my own experience, we will attempt to fill out this matrix.

(4) Finally, we will discuss challenges and opportunities that are unique to a data science organization in the _research_ space. Broadly speaking, these include the instability of research practices (compared to manufacturing, for e.g.), the phenotype of people motivated to solve these the problem classes aforementioned, the need (or not) to hire individuals with dual quantitative + biology/chemistry background and where to find them, the cycle time necessary to demonstrate value, and how to demonstrate value in a revenue-consuming cost center (research), rather than a revenue-generating profit center (e.g. commercial)

By the end of this session, data science leaders at biotech and pharma companies who attend this session will be equipped with frameworks for for thinking about data science problems in the biotech and pharma research space. Executives who are unfamiliar with the research space of data science problems will walk away with a broad, high-level overview of data science problems in research and how to frame and understand their value. Individual contributors should walk away with a greater understanding of how their role fits into the broader (and rather universal) mission of research data science teams.

Background Knowledge:

This tutorial is primarily targeted towards data science team leads. Secondarily, executives who wish to gain a better understanding of research data science will also benefit. Individual contributors who are seeking to broaden their scope or, at the minimum, better understand the positioning of their role within a broader framework, will likely have much to gain as well. No technical pre-requisites are necessary as this is not a technical tutorial.

Bio: 

Bio Coming Soon!

Open Data Science

 

 

 

Open Data Science
One Broadway
Cambridge, MA 02142
info@odsc.com

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Youtube
Consent to display content from - Youtube
Vimeo
Consent to display content from - Vimeo
Google Maps
Consent to display content from - Google