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Ten things to look for when hiring a data scientist

Denis Arnaud

Head of Data Science Development, Amadeus IT Group

Occasionally, data scientists are perceived as a commodity, much like pieces of software. The thinking goes “if we have (big) data, software, and a piece of data science, we should be all set, shouldn’t we?” But if things were that simple, there wouldn’t be a shortage of data scientists! Not all data scientists are created equal – they have different skills and strengths, and hiring a team with appropriate and complementary skills is vital for any successful business intelligence programme.

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I have listed below 10 skills that we deem important in our team. I have grouped them into two categories – scientific approach and business orientation – though all are equally valuable in the ideal data scientist. Having a real range of skills and attributes is crucial; as the managing director of a small company which specialises in analysing non-consistent sources of passenger data advised us, “do not hire people who are just a computer scientist or just a data analyst. The bottom line is that a business-oriented person will be unable to tackle the data, and a scientific-oriented person will not see what to do with the data.”

Scientific approach Curiosity

Every single day should bring some new learning. A data scientist should strive to learn something new as often as possible. Go out into the world, explore, be adventurous, and bring something new to the table.

Dedication and patience

A data scientist will often feel lost in the middle of a data ocean. Don’t lose sight of the objective when trying to assemble pieces of data, and don’t try to move too quickly, which can result in errors. Data relies on getting the specifics right, but ensuring this takes time and energy. The data scientist needs a good dose of patience to get through it.

Rigour

Data scientists have a responsibility towards the customer. Any data which may eventually reach the customer should be perfectly curated; any analysis should have been cross-checked with peers and with business-focused people.

Creativity

Rigour should not hinder creativity. On the contrary: once the delivery to a customer is secured, then the brain should unleash its full potential of creativity. There should not be any barrier, other than legal and moral of course, to design new models, processes, algorithms, analyses, or perspectives. A data scientist’s imagination is his or her only limit.

Willingness to make an impact

Any scientist aims to make sense out of the reality stemming from the data. But, what for? Often, this goes beyond a specific project and looks to provide a positive impact across organisations, countries or individuals, by making certain data or findings universally available.

Business orientation Looking at the big picture

We should not lose sight that our daily work serves a bigger endeavour: embracing our employee’s business objectives and making customers happy. These are our main drivers, reminding us what to focus on when data requires us to get our hands dirty. A business-centred mind is the lighthouse guiding the data scientist at every paddle stroke in the data ocean towards customer’s satisfaction.

Love of data

A corollary of the business orientation is the willingness to care for the data. I don’t mean the raw data per se, but rather the potential of what the transformed data can unlock. In other words, it is the ability to detect a diamond in a piece of coal.

Feel responsible towards the customers

Since what we produce eventually reaches a customer, it is of the utmost important that that we consistently produce well curated data, and of course well constructed and relevant analysis.

Act as part of a team, group, company

Data scientists do not work in silos, insulated one from the other; they are part of teams, themselves part of business units with such diverse groups as sales, marketing, finance, operations, product management, strategy and legal. They must interact harmoniously with all of those groups. Our boss always tells us: “Sales should love you. If they don’t, then you’ve got something wrong; please reconsider your way of doing things!”

Understand how to prioritise tasks based on revenue generation

As part of a company, the data scientists should take their share of revenue (and margin) contribution. In case of any doubt on how to prioritise projects or tasks, ranking them by expected revenue (and margin) helps a lot. Data scientists should not be averse to helping their company make a profit out of his/her work.

Businesses can outsource nearly any process. Its understanding of the market, however – how supply matches demand, the dynamics of what customers want versus what is on offer – cannot be outsourced. This emphasis on understanding the market should be the core of any business. It is incredibly important to recognise business intelligence and data scientists as vital parts of the business: as we better understand the market, our credibility will improve. Customers will trust us more and our business will grow. Data scientists are much more than a commodity: investing in them means investing in the future of your business.

Editor’s note: It’s been labelled the sexiest profession of the 21st Century, one where demand has raced ahead of supply, a hybrid of data hacker, analyst, communicator, and trusted advisor. Data scientists are people with the skill set (and the mind-set) to tame Big Data technologies and put them to good use. But what kind of person does this? Who has that powerful –and rare- combination of skills?  In this series, Amadeus’ team of Data Scientists seek to unlock the answers to those questions and their impact on travel.


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Big Data