Business

Spot the difference?

How to hire a data scientist.

How do you hire a data scientist? Tough question eh.

It is a new field — not many are being produced at the rate at which they are needed — most claim to be one and many do not know they are one..

A shortage of 1.5million data scientists predicted by the end of 2018 alone.

In the last 3 years I have helped perform DD on many data science companies, I still continue to hire in my own and speak at many forums. Here are my tips.

BUT FIRST

Data engineer-someone who does the ETL, cleans, munges, keeps the data prepped and ready for the data scientists to work on. Basically they have an end goal to work towards-plug the data warehouse/lake to an infrastructure that will work upon it further. Statistics, database, scripting tools knowledge required. The potion stirrer.

Someone who learns and knows and themselves breathe data.

Data scientist-someone who uses the prepped up data, uses creative multidisciplinary and interdisciplinary approach to solve business problems. They may or may not have an end goal such as a given, fixed problem to solve. Machine learning, creative problem solving, Bayesian knowledge, statistical knowledge required. The one who breathes life.

Someone who makes data a living, meaningful, organic mass that breathes itself.


In an ideal world these would be two different people but we do not live in an ideal world so they are not.

Think of it this way, when the first movies were made - the actor would do their own makeup, their own stunts and sometimes even wrote their dialogues.

Today specialisations and separations have evolved. Right?

Similarly — currently the protagonist of the data science movie does all of above and even brings their own parasols and outfits for the set.

One repeated problem I notice is that people fall in love with personalities, when they find a really cool, awesome, scientist- they build plans around them. Great- it is indeed a seller’s market given data science is repeatedly one of the highest paid professions in the world -but be wary- this can be very detrimental. End of the day if you are in an organisation with a goal or building a business, so, most likely you have a business target to achieve.

So, do not create roles around people. People should fit the role you need to be filled.

( Although in the end all of it depends upon the unique business you are building and I suppose there are no golden rules )

Here are some tips I follow.

Make the hiring manager fill out this questionnaire before hand. Some of the points are common sense and some are not. Usually CTOs and hiring managers get swayed by personalities ( positively or negatively) and sometimes lose the objective with which the resource has been hired. Especially for a nascent discipline such as data sciences - most comparables end up being “soft points”- performance quantification is tough.

So being clear about what you need out of the role is important.

Make the manager fill this out ahead of even advertising for the role, the managers will oblige as they are managing one of the most expensive roles in your organisation-cost wise and impact wise.


1. Who is this new hire?

( Interdisciplinary, problem solver, someone who achieves exactly blabla )

2. What will they do?

In first 1 week- 3 weeks- 6 weeks-12 weeks and then at steady state

3. Once hired what exact deliverables will you evaluate?

1 month- 2months- 3months- steady state

4. What KPIs will you use to evaluate the work?

Speed, efficiency, creativity, customer feedback, peer applause etc.

5. Given data science is evolving what skills should this person pick up?

In 3-6 months-1 year

6. What other ways can this role spin given business alternatives or pivots?

Help brainstorm and anticipate what other skills they may have.

Use these clues to create adverts and let people know about the role.

Finally when interviewing to see who fits the puzzle- the last response #6. can help you check for specific kinds of flexible skills or “nice to have skills” in interviewees ( tie-breaker maybe?)

Then once role is filled up, have HR or manager set up monthly/bi weekly meetings to check if the Boss is updating above framework frequently and make sure it is done so all evaluations are objective. This is in best interest of both employee and manager - an unbiased understanding of their own contribution to data sciences.

Startups need to be agile- this means this questionnaire should change as the business evolves. If employee is already hired, bring them into the discussion and incentivise them by sharing transparent goals.

Many companies have good standard HR procedures to deal with these but when dealing with data scientists - important to be flexible and change the questions so it reflects the scarcity of the data science market as well as the business objective they are meant to fulfil.

In general ask your interviewers to check for interdisciplinary people, flexibility and an interest to solve challenges. Very picky people who say “ I do not clean data- I only do Machine learning” — normally they go straight to my NO list. You need someone who will own it all and deliver the results you need and they need, to move together in a fast changing environment.



Good luck and if any follow up questions on further technicalities - hit me up !

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