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Jacqueline Nolis is a Data Science consultant, who helps companies like T-Mobile, Expedia, with their data science problems.She’s got an undergrad in math. Masters in math. She got a doctorate in industrial engineering and then started working as a consultant. For the last ten years she’s been doing data science consulting for all sorts of companies and leading data science teams.

Emily Robinson studied very related fields of statistics. And that's where she started programming in R, went on from there to get a Master's in organizational behavior and then did Metis, which is another data science bootcamp.Went on to Etsy DataCamp. And now she is a senior data scientist at Warby Parker. She got interested in data science because quantitative social sciences are a very good background to lead into data science.

Episode Links:

Jacqueline Nolis' LinkedIn: https://www.linkedin.com/in/jnolis/

Emily Robinson’s LinkedIn: https://www.linkedin.com/in/robinsones/

Emily Robinson’s Twitter: @robinson_es

Jacqueline Nolis' Twitter: @skyetetra

Emily Robinson’s Website: https://hookedondata.org/

Jacqueline Nolis' Website: https://jnolis.com/

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Outline:

Here’s the timestamps for the episode:

(00:00) – Introduction

(04:08) – There's just, clearly, some desire in the world that people are data scientists, or if you're a junior data scientist, a desire in the world to be one of these senior data scientists, giving talks at conferences and joining the community. And so we just noticed organically that this is happening more than us making some grand observation about the state of the world. You bring up the current moment also recognizing, how May I become even more valuable to employers? I may end up having to do a job search. What can I do to prepare so that I can be an attractive candidate to different companies?

(06:23) – The book was put up into four parts, and the first part is, basically, what is data science? What does it look like at different companies? How do you find jobs? What does the interview process look like all the way up to negotiating an offer? So that's the first half. The second half of the book, and the third part is around settling into your job. Putting a machine learning model into production. And dealing with stakeholders. And then, finally, the last half is about when you start settling in it's about continuing to grow by joining the community, handling failure, which is pretty much inevitable when you're a data scientist going on to a new job. And then the final chapter is what are the things you can do even after you become a senior data scientist. So Management, independent consulting or being a principal data scientist. Finally, actually we have an interview appendix with over 30 interview questions, example answers.

(08:51) – No one really knows what's happening. No one, or for the last two months, no one really knows what happened. No one knows what's going to happen for a while. That we're just in a really uncertain time. We don't know if your company is going to be around in six months, everything's more uncertain.

(09:57) –A lot of companies are putting on hiring freezes in general, except for very critical roles.

(12:18) – Each one of those stakeholders has a different goal, whether it's to make their engineering stronger, to make better decisions, to make their company go to a better place in the long term. And how you work with each one of these groups of people really will differ based on who they are and what their goals are. So we break down that a lot.

(15:40) – Some of the key communication strategies include messing up a lot until you remember how you messed up the last time, and then get a little bit better. And you do that for 10 or 20 years. And eventually you're okay. Being consistent. Creating a consistent framework for how you share things. You have to adapt your strategies.

(18:01) – The idea of how you prioritize this work thinking through a lot of the prioritization and deciding what work to do when that's really important to good stakeholder management.

(19:43) – Failure can come in all shapes and sizes. For me, I find one of the most difficult types of failure is that when you're a data scientist, you generally have to get people excited about a project before it starts. You have funding from people, and then you start working with the data. And it turns out that data doesn't have a signal in it. If you can't find it with a simple model, you're never going to find it. And that's a really big source of failure in the data science field.

(20:54) – So it's also worth thinking about, as a team, maybe not taking on only pie in the sky, very high risks, new cutting edge projects and balancing that with things that you're more confident you can deliver because that can help show people the value of the team. And then, hopefully occasionally, one of those riskier projects does pay off and it will probably pay off in a bigger way.

(22:38) – A lot of the work you need to do to handle a failure really starts long before the failure actually occurred. Companies do have different cultures around failure, and at some places it's not seen as valuable, you might be punished for it.Try to understand if that company has a culture of learning and ongoing feedback, because you do want to be at a place where it can be safe and understood that sometimes things do fail. Startups are more comfortable with failing fast and frequently because startups are lean and exciting.

(27:40) – These softwares to monitor their employee's computers, which will take screenshots every 10 minutes, it hugely invades privacy. You should know what outcomes you're striving for. What success looks like there, trust your team to do the work well, to give them the flexibility. We're not just working remotely, we're working remotely in a pandemic. And having that human understanding that people are going through different stuff.

(35:57) – I am a big component, a proponent of doing public work. In my free time, I've picked up art. So I've been doing a lot of watercolor and oil pastel, and it's been nice to just have something that is totally not tech to put a little bit of my heart into.

(43:05) – At the current moment, it's certainly riskier to leave without another job lined up. You could just ditch the system entirely and become a consultant and work as a freelancer, which is what I've been doing, which can have a huge payout and huge opportunity, but also is incredibly stressful, very risky, and just almost impossible to do right now, given the virus. I really do not care for giant tech companies to come out with giant technology and we're supposed to be excited about it. I find that inaccessible. I really love seeing new projects, new things people are doing. But what I get very excited about, too, is when folks start sharing their side projects or blogs, or sharing some of their work, it's cool. There's more to be done with other groups including people of color, but I've also seen some meetup groups and other efforts for that. So that's what's exciting to me.

(53:59) – My call to action is to try to find a way to help people. That's why we wrote the book. It was certainly not so we could get fabulously wealthy and retire early. Don't take conventional wisdom and assume because someone told you it has to be true, including us. Challenge conventional wisdom a little bit.

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How To Build A Career in Data Science with Jacqueline Nolis and Emily Robinson

HumAIn Podcast

How To Build A Career in Data Science with Jacqueline Nolis and Emily Robinson