Yael Garten, Director of Data Science at LinkedIn, shared the following list of behaviors and qualities that distinguishes exceptional data scientists from the average performers.
1. Request context and envision the answer before you start the work. Average data scientists do what they’re asked and begin building prematurely when often the real question to answer or data product to build is still not understood. Great data scientists, on the other hand, act as true owners and partners, and solicit more context, asking “assuming I had the answer to that question, what would you do with that information? Assuming I built this data product, what then? How would you use it? What would it look like? Who would it impact? How would it integrate into existing workflows or products or projects or other knowledge?” This probing ensures the question is worth answering and the product is worth building and clarifies the data and methodology and timeline needed to complete the work.
2. Iterate and collaborate. Some data scientists lock themselves away in a hole only to return much later. Great data scientists iterate, soliciting feedback, brainstorming, and breaking work into chunks as they go along. Academics at times work for months with no milestones or peer review. Great data scientists avoid this by iterating, partnering, and sharing, all of which enable them to course correct and ensure the output of the work is integrated because it makes sense, is timely, and is supported by the broader organization.
3. Be proactive. Data scientists have to stand as the voice of the data. Sometimes this is hard. But great data scientists are vocal evangelists who, via excellent work and earned respect, are able to drive change by being proactive and true to the data and the needs of the problem at hand.
4. Solicit help and feedback. Data science is hard, and no one person is great at everything. Top performers identify what they’re great at and ask for help in other areas. They improve and others have been able to contribute to creating sound output. It’s win-win.
5. Trust their gut on importance, impact, and feasibility. When they’re dealing with massive amounts of messy data, multiple objectives, and a fast-paced world with deadlines, the key to impact is having a gut for what will be impactful and for what the best way is of getting to a prototype that validates whether there is something worth pursuing — before continuing to invest resources.
6. Exude humble self-confidence on how to use the data. A winning combination is a data scientist that blends strong opinions with personal humility. I encourage my data scientists to do what is right for LinkedIn members by having a clear perspective on what action the data is telling us to take, conveying a compelling recommendation to business partners, and at the same time acknowledging the additional considerations, contexts, or priorities that they may be missing.
7. Prioritize. Recent graduates lack the necessary experience to do this well in industry. But any practice in doing this helps, and awareness that this should be a focus area when entering the workforce is essential. Great data scientists ruthlessly prioritize what they spend their time on, find ways to automate whatever can be automated or optimized, and carve out sacred time for innovation, exploration, and opportunity identification.
8. Display technical virtuosity and resourcefulness. Great data scientists are resourceful. They know how to leverage tools, technologies, and methodologies in accurate, rigorous ways. They accomplish their goals in the most context-appropriate ways. To do so, they are flexible with rapidly evolving technologies and know that the level of investment in code and tools will differ depending on the business context.
via Forbes, 29th November 2016.