Data Analyst @ Start-Up
Demystifying Data Science Interviews Series
This is the second article in the demystifying data science interview series. The series talks about the journeys one would take in order to land into data science roles. You can also check out the first article here.
Abigail has just graduated with a Bachelor’s degree in Biotechnology. During her undergraduate studies, she found herself gravitating towards data science/analytics focused courses and really enjoying them, especially the data exploration and visualization aspects. After graduating, she was looking for her first foray into the burgeoning field of data science. She starts applying to various data analytics roles.
Abigail, being a new graduate didn’t have any work experience in the field. However, she had extended several of her data science course projects and built some of her own. She had worked in the science labs, running experiments and analyzing those results. She put together a website to showcase her projects and a resume to detail her course and lab work, and to highlight the relevant data work she had accomplished.
Soon enough, a recruiter for an early-stage startup responds to Abigail’s application.
The company has a SaaS product that helps customers store media files. The business has started gaining some traction, however, the company leaders are hoping to expand quickly into a range of complementary services to upsell customers. A data team is being put together to build out the data analytics function — so far an initial data lake has been set up with their first Data Engineer. A couple of people from various departments have been spending time looking at the gathered data, building dashboards, but data analytics is not their forte. Hence, the recruiter explains to Abigail that they now need someone with the skillset to explore and make sense of the data, looking for patterns and insights. Of course, there is an opportunity to grow as the startup grows.
Abigail is very interested with this initial conversation. However, before we advise her to take it up, let’s take a step back. Using the important factors we laid out in the first article in this series, let’s see how Abigail should evaluate this exciting opportunity with this early-stage startup.
Company Maturity
This is an early-stage startup, hence it is at a very immature phase. Yes, it has started gaining some traction with its product, but it is still a race to survive as future success isn’t guaranteed. A larger competitor could potentially expand into their space and eat their lunch!
Great! What does this mean for Abigail, our aspiring Data Analyst?
Well, this could be a great opportunity for Abigail to come in and take ownership over a growing function, learn a ton from working with a range of people — data engineers, marketers, salespeople, and executives.
Ok, that sounds really lovely and all — are there any downsides?
Like most things in life, there is a good and bad side. On the bad side, this company may not get to product-market fit before they run out of funds. Gaining traction doesn’t equate to longevity in your business. Abigail’s first trip down startup land could come to a quick end. Also, there isn’t anyone necessarily to hold your hand and show you the next steps in an early-stage startup — more often, it is very chaotic. Abigail has to be able to thrive in such an environment with its ever constantly changing demands. In addition, taking initiative will be something that would be expected of her if she is the first Data Analyst hire.
So Abigail should be aware of both the potential freedom and responsibility that working at an early-stage startup affords her. It may be a good experience to get under her belt but startups aren’t for everyone.
Industry
The SaaS industry is currently an ever-growing industry with companies being created regularly to provide services at reasonable monthly prices. It has its own sets of challenges and dynamics.
What does this mean for Abigail?
To better understand the industry dynamics, the business models, and the likelihood of success, Abigail will have to research SaaS businesses — blogs, and thought leaders on Twitter, and SaaS communities, etc. This will give her more perspective and context for how to properly evaluate the company’s current status and progress.
She will have to prepare the appropriate questions based on her research into the industry. Armed with this research and questions, she should be able to gauge the company’s performance based on the industry’s context.
Role & Availability of Data
Just as data can be made available via multiple avenues (web scraping, purchases, or coming from the product itself), it also isn’t limited to single-use of purpose. It could be the product or it could serve as the growth engine for an existing product.
In this startup’s case, the data is available from customers using their product and it can serve as a potential growth engine. Gaining insights into how their customers are using the product would help the startup understand them better. This could lead to refining existing products, cleaning up the sales funnel to ensure more potential customers make it through, building complementary or more expensive services, etc.
In addition, there are privacy considerations as well as potential regulations depending on the industry. The company can still collect data on how its customers interact with its products in an anonymous method. Their focus isn’t necessarily the individual, but their customers as a whole.
In a general sense, the availability of data is controlled by the company itself. This potentially gives it a lot of latitude in how it deals with this data.
What does this mean for Abigail?
Working as a data analyst for a company using the data from its own product’s use is a pretty interesting position to be in. Abigail in her data analyst position could help explore this data, shed light on underlying patterns and build machine learning models to explore various relationships within the customer data. She could also recommend additional data they could collect for further insights.
Bringing all this together to recommend compelling new products or services, enhances to the UI/UX that improves the business is a powerful story to tell future employers.
Abigail’s Role within the Data Stack
Now let’s get back to Abigail.
Abigail is looking for her first role in the data science Field. A data analyst tasked with analyzing a company’s own data is a great starting point. She will be in a position to use her growing technical skills, while working with domain experts, engineers, product managers, etc will be a great learning opportunity for her.
She will be able to leverage her data manipulation and visualization skills to help the business better understand how their customers are interacting with their service and how they can better serve them. What a potentially great way to start your career in this space!
Being in an early-stage startup allows you the opportunity to contribute more than what your title implies you do. As Abigail learns more about the industry and the business, she could find new ways to leverage the their data or bring in 3rd party data. She could help build out the data team over time and one day lead it. There are tons of potential opportunities down this path.
The Data Analyst Start Up Interview Process
Abigail having gone through all the above attributes is super excited about this opportunity and wants to move ahead with the interview process. Let’s take a look at what she could expect:
Step 1: Apply or Recruiter reaches out
Abigail already applied and the company’s recruiter reached out to her.
Step 2: Initial Interview
You have an initial non-technical interview with the recruiter to get an understanding of what the company is looking for, the culture, etc. On the company’s side, they are trying to understand what you are looking for in your next role and whether or not it is a fit.
This initial conversation can be a very soft skill focused with some technical questions asked. Recruiters want to verify that you are interested in the opportunity and get to know you a little bit. Sometimes, they may ask generic technical questions or specific tools or languages you are familiar with.’
NOTE: Always remember to ask the recruiter what the interview process looks like from here on out.
Step 3: Technical Screen
The next step is to assess your technical skills — this can take the form of either a call with a technical staff member where you code in a screen-share, or it can be a take home assignment.
In Abigail’s case, perhaps they have a dataset they want her to explore, visualize and summarize her findings. As a test of her communication skills, perhaps they will have her present this as part of the next step.
Step 4: One Site/Remote Zoom Calls
Usually the final step is an opportunity for the larger team and you to meet each other.
In the before times, you’d be expected to take a day or couple off depending on how far you needed to travel for the onsite.
These days, companies are more likely to conduct video interviews either condensed together in one day or spaced out over several days.
Regardless of the format, this step is pretty rigirous — you’ll be asked a mixture of technical and behavioral questions to evaluate how strong you are technically and how good of a fit you would be with the rest of the team. Make sure to bring your A game!
LAST NOTE: The above is simply an example of how the interview process may look like. Each company is unique and may have its own twist. For a very early stage company, they may either work with external recruiters or the founder/technical leader may reach out to you directly.
What is important is that you walk in with an idea of what they will ask of you, be prepared to ask the questions you need to ensure you make the right decision on where to take your career next.
All the best to Abigail and the start of her exciting career in Data Analytics!
Thanks for reading! Next, we will continue this series with other scenarios where this same framework can be applied. Until the next episode!
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