In our recent blogs, you’ve seen how data science differs from big data and how data scientists may not have standard career paths in this rapidly developing field. But what is it like to work with Square Root’s data scientists on solving your company’s biggest problems?
CrowdFlower recently released this infographic on the data behind being a data scientist, and we went to our team to find out what holds true at Square Root.
Top 3 tools:
- Excel 55.6%
- R 43.1%
- Tableau 26.1%
According to Mark Schwarz, who leads our data science team, “On a tool level, SQL is the lingua franca for the team. Outside of that, each person has the flexibility to use what works for them to get the job done. Toolsets in our SaaS product are standardized around Python and SQL. Python and R are popular for analyses.”
At Square Root, our data scientists work with over 120 data sources and produce 4,000 different KPIs each day. Each data source is a different data domain: customer satisfaction, sales, aftersales, etc. Efficient tooling and automation choices make it possible for a small team to manage lots of data complexity across many data domains.
Tools are critical, but tools aren’t as important as people. According to Schwarz, “Machines can’t define problem statements well or make the first decisions about what is clean/unclean data. We need people to decide what the machines should do. The data scientists who are most successful can bring both machine operator and people/process skills to the table. Everyone on the team needs to be able to do this.”
66.7% of data scientists say cleaning and organizing data is their most time consuming task
According to Mark Gorman, a data scientist at Square Root, “As a data scientist, your insights and recommendations are only as good as the data driving them.” To that end, our team has created strong, clear processes to ensure data cleanliness. Automation allows us to stay lean and manage resources well while enabling our team to focus on more strategic work.
Data scientist wishlist:
52.3% want their organization to set clearer goals
While goal-setting is an internal focus at Square Root, a daily challenge is setting clear goals with our customers. Some organizations are used to asking the customer what they want, but instead, we ask, why do you want what you want?
Gorman told us, “There’s no better way, as a data scientist, to understand business problems clearly than to meet and speak with the business stakeholders themselves.”
Another member of the data science team, Qiong Zeng, agreed: “Each data scientist is required to have strong communication skills and is intentionally put in a client facing role. We all have good working relationships with clients and strive to understand their business in full.”
But what’s missing from this study?
100% of our team actually says “the worst that can happen in our jobs is to find a solution that is not helpful.”
According to Joachim Hubele, another Square Root data scientists, he frequently receives requests from customers in order to answer a specific question. Half of the cases are simple and straightforward, but the other half look straightforward only at first glance. There is always room for interpretation. What does he mean by this? What does he need this for? Oftentimes, there is a suspicion that the customer wants to solve a different problem, and the analysis he is asking for may provide him the correct information but the incorrect solution.
So, Hubele makes a trade-off: when it takes less than an hour, he produces an analysis or a report as a discussion basis. This is helpful because it can either validate or reveal the deficiencies of the request quickly. “Then I always call,” says Hubele. A lot can get lost in translation over email, and phone calls help establish more context and a sense of urgency. In this case, he discovered the client indeed needed something different from the original request, which cut down his expected workload by 95% and he was still able to deliver a valuable solution.
Our data scientists are happiest when the work they are doing provides real business value to our clients.
Our data science team aims to make people’s lives easier, and no one is happier than our clients when they meet this goal. “Launching our new customer loyalty initiative would have required me to use twelve different reports to review all of our program metrics. With the help of the Square Root team and CoEFFICIENT®, I was able to see everything in one consolidated report.” – Warda Shah, Nissan Canada. See the full Nissan Canada case study here.
Our cutting-edge data science team works every day to deliver value to large retail organizations. Want to leverage the power of our team to drive your business goals? Check out our solutions.