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Interview with Natalie Spare – Project Solutions Data Scientist On Teams and Analytics Translation

Natalie Spare Project Solutions Data Scientist at Diveplane AI
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Welcome Natalie Spare! Thank you so much for sharing your knowledge and insights in tech! Let’s start with a simple question, what do you do?

I’m a project solutions data scientist at an awesome artificial intelligence startup in Raleigh, North Carolina called Diveplane.
Oh wow, that’s great, I actually looked into Diveplane. What a great company! Diveplane was named among the Top 10 Startups to Watch by the North Carolina Tech Association. Can you tell me more about how you found yourself working in solutions architecture and analytics translation?

I actually stumbled into this role by accident – it’s not something that I sought out, but now that I’m doing it I realize how important it is. The concept of a solutions architect or solutions engineer isn’t a new thing within the broader tech space, but those kinds of roles don’t really exist yet in the data science world!
How does this apply to the role of data science across enterprise and startups?

Since data science is more of a service than a product, there has to be someone that has the ability to understand deeply technical concepts on par with a data scientist or engineer, but also be able to translate those concepts into relevant business goals for the nontechnical people on the team (and/or the nontechnical client, as well). This is especially important in startups – no matter how groundbreaking of an idea you have, if you can’t communicate to others why it’s so awesome, it’s about as good as the product not existing at all.
Where does your role fit in?

Most places you go, there is an engineering or dev team and there is a sales team. There is usually no one in between these teams to communicate on behalf of both so that everyone is always in the loop. It is as unproductive to have an engineer in a sales meeting as it is to have a sales person in an engineering meeting.

Can you share some insights into your skills and activities?


Aside from needing to be able to understand and communicate very effectively, this role also forces you to have good project management (PM) skills as well.

 

I am basically a PM, solutions engineer, communications specialist, and data scientist rolled up into one. I’m on every client call, and also in every engineering meeting. I’m in presentations with our business development guys, and I’m also in our sprint planning kickoffs.

 

I’m supposed to be able to answer the client and sales teams’ questions about the status of our product builds on behalf of the data science and engineering teams, and I’m supposed to be able to answer the data science and engineering teams’ questions about project status on behalf of the client and sales team.

 

I just had my boss, the chief commercial officer, ask me if I will meet with one of our data scientists to get a run-down of the most recent analysis on one of our projects and then circle back and schedule a meeting with him to explain the findings.

What would you say makes this job so enjoyable?


I love this role because I actually LOVE teaching and explaining things! I am fuelled by the idea that I can make a difference by helping someone understand a thing that they previously did not.

 

Like I said, I ended up in this role by accident – people kept complimenting me on my ability to explain complex topics in a highly accessible way, and I just kind of went with it! I started out wanting to be a data scientist (and still want to do data science) but now I’ve realized that what I am actually drawn to is the discovery and communication of the unknown.

 

It’s a fun challenge for me to have an engineer explain an algorithm or a piece of math, and then to take that and turn around and translate it to a 100% pure business person. When they say, “wow, I totally get it,” it is super gratifying and makes me feel like a superhero.

How does analytics translation fit in with future skills development?


As far as where this role is headed as #futureskill, it’s obviously an important addition to any team, but the idea of an analytics translator transcends the work environment. It will become a super necessary part of the public marketing process for data-related services.

 

I think that in order for AI to truly take off, we have to have AI evangelists like me to translate those topics into understandable information for the general public. Otherwise, there is misinformation galore and no one to correct it or challenge it.

 

People literally think that Skynet is just around the corner… meanwhile, we are still working out the problems of compute resource constraints, large dataset dependencies, and our current inability to truly do unsupervised learning without a TON of prior model training. We are not as close as people think .

Are you still learning? How are you further developing your career in this area?


I think that the best way to continue to develop my skills in this space is to keep immersing myself in all the technical stuff. I actually come from a marketing background and am only recently a convert to the data science field. I taught myself to code in 2016 while I was working as a consulting analyst. I started to make little improvements to my workflows and changed that organization to be more data-centric.

 

Three years later, here I am at this cool AI startup! I do think that my background helps immensely for my role, since I was trained primarily in business and secondarily in data science. It helps me to see everything through a business/utility-focused lens. I will continue to improve my presentation skills, and also my technical skills. Mostly through practice and online courses!

Can you share any skills, best practices and recommendations for others looking to acquire these skills or work in analytics translation and solutions architecture?


Communication is a big one! This is such a nebulous term, too. What I mean is audience-centered communication. Know who you are talking to and then adjust your explanation for that person. Be able to explain a topic on various levels: 5-year old, 15-year old, technical adult, non-technical adult, and grandmother. It will force you to get creative with your thinking and start challenging your normal thought patterns.

 

Think about the big picture. Technical people have the (understandable) tendency to think in terms of compartmentalized tasks. Similarly, sales people have a tendency to think in terms of leads and commissions. Both make sense, when you think about what they do for a living. As a translator, you have to practice thinking about things from a 50,000 ft view and visualize the entire value chain. The WHOLE pipeline, not just the part you are responsible for. That will allow you to understand and know what questions need to be asked, of whom, and when.

 

Always ask EVERY member of your team to provide input. It’s often the quiet ones that have the best ideas or the most burning questions. It’s important to give everyone the chance to speak and ALWAYS ask if anyone has questions or concerns. Then, at the end of every meeting, always be sure to ask “is everyone on the same page?” or some variation of that question. It’s super important to make sure that all team members are clear on the mission, action items, objectives, etc. Even if you aren’t in a managerial role, you will quickly establish yourself as an admirable professional if you ask these sorts of questions. Similarly, if you’re in a client-facing role, always make sure to periodically ask, “Does that make sense?”

 

Question everything! Yes, at first glance, a person who knows a lot of tech jargon must know what they’re talking about, right? Not necessarily. Those people can be wrong (and are wrong, frequently), and no one will ever call them out on it if they’re not forced to explain themselves in a greater depth.How boring would everything be if we all just accepted what the smartest-sounding person said? I’ve been in situations where I asked someone to explain something more, and then one of the other engineers chimed in and said “really, that’s how it works?”. That second guy was apparently just too afraid to ask previously. Always ask and always challenge (politely).

 

Test yourself! Schedule separate meetings with both your engineers and your sales people and try to explain stuff to them on behalf of the other party. Another great way to do this is to grab a willing friend who doesn’t work in your field and then do your best to briefly and succinctly.

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Natalie Spare Project Solutions Data Scientist at Diveplane AI

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