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There is a lot of buzz not only around hiring data scientists and even chief data officers and the skills they need in understanding how to extract data for business needs, but also how to tell a story with the data and insights they uncover.
Business acumen is as important, if not more, than hard skills. Data scientists represents an evolution from a pure business or a pure data analyst role. They need a solid foundation in computer science and applications, modelling, statistics, analytics and math. Pallath Paul, Director, TIP BIT BI, SAP Labs India, says, “Machine learning and data mining are highly specialised areas and need people with a skill-sets which is rare today, and so you have people with peripheral views doing this.”
Core skills to look at
Ideally, while looking for a data scientist, companies look for skills like creativity, passion for finding interesting patterns in data, and the ability to design algorithms and models, rather than relying on out of the box algorithms alone.
The ability to understand the domain, and find analytics use cases is also a key requirement for a data scientist. What sets a data scientist apart from a data analyst is strong business acumen, coupled with the ability to communicate well with both business and IT leaders in a way that can influence how an organisation approaches a business challenge.
Good data scientists will not just address business problems; they will pick the right problems that offer the most value to the organisation. It is essential for a data scientist to understand the domains of programming, machine learning, data mining, statistics, and hacking--in the positive sense. These are keys to getting in and grabbing the data one needs.
A good data scientist needs to understand his domain, whether it’s science, engineering or business. He needs to be able to cut through the myths associated with big data.
Richard de Souza, Head-IT (Projects & Central Support), Mahindra Group says, “A data scientist should be someone who likes numbers and people.” As for business, one has to choose which aspect to be prioritised, and what’s important for the data. One has to take a low-hanging fruit mentality with data.
The data is only getting bigger, and it’s getting more and more important for businesses to stay ahead. The future will involve many data scientists, each with his own domain. While a single data scientist might suffice for an SMB, one is absolutely not enough for a large enterprise going into a future dominated by the Internet of Things and ever-increasing floods of information.
Evaluating Criteria
- Inquisitiveness. Stare at the data and spot trends. It’s almost like a Renaissance individual who really wants to learn and bring change to an organisation.
- H unger to Explore. Asking questions, doing “what if” analysis, questioning existing assumptions and processes.
- Communication: Selling conclusions and recommendations across an organisation’s leadership structure. All the charts in the world won’t make a difference if one can’t communicate to product managers or executives why one’s findings matter.
- Ability to understand and use the analytics tools made available in the organisation.
- Ability to understand the theoretical concepts behind the statistical techniques being used by the tools.
- Significant exposure to business domain and customer insights.
Creativity and Curiosity
A data scientist’s job is a consulting role, and no one person can ever become an expert. The data scientist ties everything together, maybe, but he’s an expert in none of it.
A certain element of creative strategy should be considered while hiring data scientists. de Souza says, “Curiosity and creativity are the most important things to look for while hiring data scientists. A sense of wonder is key.
Good data scientists have a sense of wonder about the world and are happiest when they are discovering how something works or why it works that way! Creativity is king for a great data scientist.” Xerox Research Centre India (XRCI), an organisation primarily in Analytics, is innovating newer ways of looking into data.
XCRI has combined traditional computer science with ethnography in terms of hiring data scientists. Manish Gupta, Director, XRIC, says, “In computer science, machine learning is turning out to be a pretty hot area.
We hire people with computer science experience, with specialisation in machine learning. Other areas that we look at around data are information retrieval and data cleansing to remove the noisy part of the data.” “We have employed people with deep expertise in statistics; to support all the research that we conduct, we hired ethnographers who can observe what’s going on in the real world”, adds Gupta
Ethnographers help Manish and his team with observations as to how a workflow or process should be modified to see how human beings behave with data. Phillip Beniac, Regional Vice President, Qlik Asia Pacific, questions, “What if we can all be a bit of a data scientist?
"I believe information can change the world and every user contributes to that transformation. This is what we call Business Discovery-- enabling every user to easily analyse data and experience that “ah-ha” moment of discovery.” Democratising analytics of data-- whether big or small, so that decision makers can get access to the data is key to unlocking the true value of data.
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