What do you need to do before hiring a Data Scientist?

Artificial Intelligence brings a promise of exponential growth and taking your business to new heights. No wonder there is a lot of excitement around the application of Artificial Intelligence (AI).

Many companies rush to hire their first Data Scientist or build a Data Science team immediately. Their enthusiasm is understandable as they want to innovate with data without being out-competed by the market. However, these early missteps and false starts are causing a massive opportunity cost to companies, and Data Scientists are moving on from these companies within just a couple of years.

Here are some recommendations for you to prepare before investing in the Data Science function at your company:

1. Have a clear understanding of why you want to hire a Data Scientist

You can begin by identifying the business problems and opportunities you want them to address. You don’t necessarily need to have large amounts of data, but you need some data related to the identified business problems. For example, a spreadsheet with a few hundred thousand rows with the right data attributes covering the majority of population distribution may be enough.

If it’s deemed too risky to begin with a major business problem, you can start by shortlisting and prioritising simpler use cases like:

• Voice-based fraud detection and reduction in the customer care centre.

• Product recommendations for an eCommerce site.

• Predicting churn for a B2B SaaS company.

Let’s ask Data Scientist to look at the data we have and let them generate the business value — is a bad strategy.

Alternatively, you can hire an independent consultant or work with a service provider should you want to test the waters first before venturing into building a team yourself.

2. Understand data science profiles and know what kind of person you want to hire

Data Scientists come from various backgrounds, like any other profession (Marketers, Designers, Product Managers etc.). Some have expertise in building Machine Learning models, while others are strong at Analytics and Visualisation. Some have only worked in Computer Vision, while others have only in Natural Language Processing (NLP). Some are generalists, while others are specialists.

Hire the Data Scientist relevant to your problem space.

You don’t need to hire a PhD or use Kaggle as your primary means to assess someone. There are good data scientists with no PhD who have never participated in Kaggle competitions.

When you hire PhDs with no commercial experience, there is a bias for the academic style approach rather than the business thinking.

PhD in a relevant discipline and participation in community initiatives like Kaggle is a good thing, but it shouldn’t be mandatory hiring criteria. That said, they absolutely have to know what’s going on in their field.

Except for a few institutions collaborating with corporates, academics don’t have the context to solve business problems. An academic person without commercial experience may end up spending and unravelling anomalies in data when all you need is a quick prototype to go to the next stage.

Your first hire should have enough breadth of knowledge about the Data Science field in general and some business knowledge. You may need a “Data Engineer” first, who is strong in Software Engineering, Data Storage, Extraction and Management instead of Mathematics and Statistics like a Data Scientist.

3. Define what you want them to do

Data Scientists are driven by applying their expertise to solve complex problems while utilising and contributing to academic research.

Job Descriptions focusing on the company instead of the problems and use cases, or the ones providing no or generic information, don’t attract much attention. For e.g. Instead of talking about your billion-dollar insurance company that might attract MBAs, you should talk about use cases like automated inspection of car damage, increasing the accuracy of busting bogus claims etc., to attract Data Scientists.

Looking for a person who knows all things Data Science is equivalent to looking for a unicorn proficient in Business, Technology, Maths, Modelling, Programming and Statistics.

You should account for overall skills, not just hard technical skills. They need to work as part of your team and fill the skills gap your team is currently missing.

4. Build a data-driven culture that favours data scientists

It’s vital to look inwards and assess if your company culture will work for or against Data Scientists. Some red flags include not having any technical or business person with a basic understanding of Data Science or not having any data and technical support in place.

If the company doesn’t have experience working with data and doesn’t make data-informed decisions, Data Scientists will struggle to convert their work into business and customer value.

You also need to work out the overall budget to support a Data Scientist or a team. AI startups with a huge VC-funded war chest may not have to worry about this. For corporates, given the scale of data and problems you want to address, these costs can run into hundreds of thousands, bringing your well-intended efforts to a standstill. Open-source software and Data Science tools are bringing some of these costs down.

Companies who fail in their AI efforts, and many of them do, end up blaming Data Scientists for poor skills, whereas, in reality, it might be them who failed to provide the right support, environment, budget and team.

5. Decide where and how they will fit within your organisation

You need an internal leader from the Product, Technology or Business function to steer, support and lead Data Science initiatives.

The Data Science team should be wrapped in a function to build cadence, accountability and a continuous feedback loop from the rest of the business.

The inevitable question of centralised vs decentralised team comes up sooner or later. There are several factors like your current processes, organisation model, data maturity and team size that would sway the decision in favour of one approach over another.

For a scale-up AI startup, you may find many Data Scientists in one team. For technology behemoths like Google and Facebook, you may find a Data Scientist per product team for data-heavy products. For corporates, you may find a centralised Data Science team accessible to all other teams. For Agile organisations, you may find a data science squad focusing on AI/ML products.

Whatever model you choose, keep the flexibility to try and evolve as you build up the Data Science function.

6. Have a supporting or dedicated Product Manager

Given a portion of PhD research is done in isolation, some business leaders believe Data Scientist does the majority of their work in isolation (picture a scientist locked in a room scribbling on a blackboard). This couldn’t be further from the truth. Data Scientists need a team of engineers, business stakeholders, project managers and other team members to deliver projects.

You should assign a dedicated or supporting Product Manager to the business problem/opportunity Data Scientist addresses. Product Managers can handle countless tasks like requirements gathering, customer insights, data analysis, operating models, legal guidelines, product delivery, pooling resources and getting things to production. Data Scientists should be involved in a number of these activities, but if there is no support, you won’t get much value out of their efforts.

Some Data Scientists with experience working with Product Managers and Scrum Masters are bullish on this idea, saying:

Product Managers want to claim a lot of credit without adding any value.

The statement could be true for some, but not all. The majority of Product Managers have never delivered Data Science projects before. They are learning and figuring things out as the field and art of Data Science product management is evolving.

Product Managers can set up alignment and expectations across the business and can be valuable in removing roadblocks.

The conclusion is there must be a clear role alignment between a Product Manager and a Data Scientist and what each member brings to the table for this partnership to work.

Each one of the above points can be a long-form post in itself. There is so much to unravel around costs, processes, skills, team, operating model and so on.

Let me know your thoughts in comments and if you want to hear more on anything specific.

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