<story><title>Beware the data science pin factory: The power of the data science generalist</title><url>https://multithreaded.stitchfix.com/blog/2019/03/11/FullStackDS-Generalists/</url></story><parent_chain><item><author>reilly3000</author><text>I really wish hiring managers read this. I am a data generalist, and have had no traction with obtaining even an interview for a data science job. I’ve setup a private JupyterHub where I run python ETL, interactive models, and dashboards. I deployed Metabase several times and have written hundreds of SQL queries. I’ve used Tableau with gigantic datasets. I built a front end serverless analytics pipeline from scratch with AWS that handles 30M events&amp;#x2F;mo. I&amp;#x27;ve demonstrably grown revenue and margins in multiple contexts with my data products. I’m working on making a fully dynamic frontend for content recommendations. I have self-taught all of these skills in the past 3 years after a decade in sales, marketing, and entrepreneurship.  What I haven’t done: a CS&amp;#x2F;math degree (mine was music), graduate work, or tech work at a household name. Lived in the Bay Area. Gotten an interview for any data job. Sigh.</text></item></parent_chain><comment><author>sidlls</author><text>None of what you describe is something I&amp;#x27;d hire into a data science role specifically. Some of those skills are skills I&amp;#x27;d expect a data scientist to have (e.g. SQL skills). A data scientist in this context has to have an understanding of basic statistics generally: hypothesis testing, modelling techniques and their applications, and performance tuning and evaluation. They also need to understand how to devise and run experiments that collect and make use of data in practice (i.e. &amp;quot;real world&amp;quot; data). I wouldn&amp;#x27;t necessarily expect a candidate to be able to derive the formulas involved, but it would be the odd candidate who truly grasped the nuances who could not do so.</text></comment>
<story><title>Beware the data science pin factory: The power of the data science generalist</title><url>https://multithreaded.stitchfix.com/blog/2019/03/11/FullStackDS-Generalists/</url></story><parent_chain><item><author>reilly3000</author><text>I really wish hiring managers read this. I am a data generalist, and have had no traction with obtaining even an interview for a data science job. I’ve setup a private JupyterHub where I run python ETL, interactive models, and dashboards. I deployed Metabase several times and have written hundreds of SQL queries. I’ve used Tableau with gigantic datasets. I built a front end serverless analytics pipeline from scratch with AWS that handles 30M events&amp;#x2F;mo. I&amp;#x27;ve demonstrably grown revenue and margins in multiple contexts with my data products. I’m working on making a fully dynamic frontend for content recommendations. I have self-taught all of these skills in the past 3 years after a decade in sales, marketing, and entrepreneurship.  What I haven’t done: a CS&amp;#x2F;math degree (mine was music), graduate work, or tech work at a household name. Lived in the Bay Area. Gotten an interview for any data job. Sigh.</text></item></parent_chain><comment><author>opportune</author><text>The one thing I see kind of missing is a math background or at least a project proving that that is in your skillset (recommendations sounds like it could fit this). There are a lot of people with a similar background to you and normally those are in &amp;quot;business intelligence&amp;#x2F;analytics&amp;quot; or &amp;quot;data engineering&amp;quot; where they are mostly writing sql queries and interacting with dashboards&amp;#x2F;OLAP cubes or setting up those dashboards&amp;#x2F;cubes.&lt;p&gt;That&amp;#x27;s perfectly fine but it&amp;#x27;s not what traditionally is referred to as data science. I&amp;#x27;m actually quite annoyed at what has been happening to the term data science lately - it&amp;#x27;s supposed to be some stats-heavy&amp;#x2F;applied-AI role but a lot of companies hiring &amp;quot;data scientists&amp;quot; are really just hiring SQL jockeys.&lt;p&gt;Personally I&amp;#x27;ve done both data science and data infrastructure and I like infrastructure a lot more anyway. And it sounds like you are somewhat qualified for that with some of your pipeline work (although big data experience is also important). A LOT of data science departments have no idea what type of business value they are supposed to be adding, are doing shitty boring work with glorified titles, or are improperly integrated with the company at large (bad productionizing processes, poor data infrastructure). There&amp;#x27;s always going to be a need for data infrastructure but the &amp;quot;data science&amp;quot; hype is going to fade once all the shitty data departments cut the fat.</text></comment>