To Patent or Not to Patent? How to Weigh the Options for Your Team (Kli Pappas, Associate Director of Global Analytics, Colgate-Palmolive)

<p>Should your team patent its data science work? With open source such an important part of the data science community, patents almost seem antithetical to the ethos of the field itself.</p> <p>But it turns out, there are some very good reasons to pursue data science patents in business.</p> <p>In this episode, <a href="https://www.linkedin.com/in/iraklispappas/">Kli Pappas</a>, Associate Director of Global Analytics at <a href="https://www.colgatepalmolive.com/">Colgate-Palmolive</a>, shares his team's process for deciding whether to patent an algorithmic process—and what benefits it can bring. Plus, he talks about why a statistical background is so important for teams that generate data.</p> <p>We discuss:</p> <p>- The transition from getting a PhD in chemistry to the analytics world</p> <p>- Finding the balance between statistical and computer science backgrounds</p> <p>- Why you should patent your data science work and how to do it</p> <p><em>Tune in on </em><a href="https://podcasts.apple.com/us/podcast/data-science-leaders/id1564587119"><em>Apple Podcasts</em></a><em>, </em><a href="https://open.spotify.com/show/0zJgKGIHZzJG1VgSDHNojP"><em>Spotify</em></a><em>, our </em><a href="https://www.dominodatalab.com/data-science-leaders-podcast/"><em>website</em></a><em>, or wherever you listen to podcasts.</em></p> <p><em>Can’t see the links above? Just visit </em><a href="http://domino.buzz/podcast"><em>domino.buzz/podcast</em></a><em> for helpful links from each episode.</em></p>