Attracting the right data science skills into your organisation can be challenging. Not only are data scientists in-demand, but once you find a suitable hire, how do you provide these highly skilled professionals with an engaging work environment?
Here at the Hub our aim is to build an open ecosystem of data scientist and data driven organisations, but we understand that attracting talent and finding the right fit for your organisation can be tricky. So to help you build your data science team, we have turned to technology recruitment extraordinaire Tina Ambrose, who shares with us her key tips and tricks for attracting and retaining data science talent.
Most organisations are looking at how they can use data more effectively in decision making. Some organisations are at the beginning of this journey, looking to hire professionals that have experience with the basic use and visualisation of data, whilst others are looking to build highly skilled data science teams. Wherever your organisation is at in it’s data science journey, there are a number of key considerations when attracting and retaining data professionals.
Be clear about your data science maturity level
Why do most people become Data Scientists? Traditionally, it’s to use their scientific expertise to explore, innovate and collaborate in order to solve complex problems.
If you are at the initial stages of adopting data science within your organisation, you may be a way off having a platform that enables a Data Scientist to get stuck into this type of work straight away, which may cause frustration for some. Be honest if this is the case! There is an experience level and insight required to effectively set the strategic direction of your data journey, some Lead Data Scientists are excited by the prospect of being the champion of data science, and have the patience to thrive on proving the ROI to sceptical stakeholders across the business.
Most Data Scientists, however, will want to know there is a clear data strategy that has the C-Suite support, tools and appropriate financial investment to succeed, empowering them to focus on what they do best; helping you solve complex problems and make better decisions.
To attract the right candidate, paint an accurate picture of problems they will be required to solve, the data they have at their disposal and the bigger picture impact their work will have on the business. Be clear about the limitations they may have to work within (tools, technologies, budgets) and highlight the areas they will be able to innovate and be creative.
Manage your bias
The pathway into data science comes in many different shapes and some of the most successful data scientists I know have followed unconventional pathways into the field.
Be sure to implement a process which mitigates your unconscious bias. E.g. we sometimes see managers who come from PhD backgrounds favour those candidate resumes, and clients from more commercial backgrounds shy away from candidates coming from predominantly academic fields.
We coach our clients on creating processes that mitigate bias. Our top 3 simple steps to take include:
- Utilising a short exercise that candidates can complete at home within 2 hours, is close to a real-life problem your Data Science team may tackle and is a fun challenge to try and solve. Make this your first step in the process for applicants, before going through CVs. This ensures you are basing your very first selection on ability, without bias.
- Making the face to face interview as close to a working scenario as possible. Focus on tackling the problem in a collaborative way, with the candidate taking the lead, and include the people they would usually work with to solve that problem. Getting more than one opinion from the team helps control bias
- Save the cultural interview questions for the last interview stage. Make sure these questions are structured to establish alignment to company values, not cultural bias.
Is your recruitment process too simple to be meaningful or too complex to be trusted?
The key skill behind striking the right balance for this is empathy. Good questions to ask yourself are:
- What does your company need to demonstrate to a candidate for them to want to work for you?
- What information do they need to get from the interview process to feel comfortable in their decision when an offer is presented?
- What behaviours will show the candidate they are valued?
Keeping top talent engaged is critical to avoid losing out to the competition. Our top 3 tips around this are:
- Create a process which allows candidates to demonstrate their skills practically, in a comfortable environment. This is your opportunity to “sell” examples of the key challenges they could be working on, if they joined your company.
- Be decisive and quick in moving to the next stage. The best way to demonstrate to a candidate they are valued is to keep momentum high and move faster than your competition.
- Encourage candidates to ask a lot of questions through the process, and make sure they have the appropriate time to do so. Interviewing is a two-way street and, let’s face it, good Data Scientists are analytical and curious by nature.
Feedback is the key to maintain your pipeline of data candidates
Providing clear and constructive feedback throughout the process is always the right thing to do, no matter what role you are hiring for.
When hiring for in-demand, highly skilled talent, it can be the difference between building a healthy pipeline of candidates and, well, not.
Giving candidates constructive feedback as to why they weren’t successful in that process and clear guidance as to the areas they could improve upon to be successful in the future, empowers them to learn and grow. It drives the same mutual respect you get from a mentor relationship.
I have seen organisations do this so effectively they’ve had their 2nd place candidate, who have narrowly missed out to another candidate twice, be so invested in working for their organisation they’ve gone through the process on 3 separate occasions. Each time, taking on board the feedback and improving on the areas outlined to them. The third and final time, being successful and going on to become one of their best performers within the team
Who wouldn’t want their 2nd, 3rd or even 4th place candidate to be so inspired by the feedback they received, so enamoured with their experience through the recruitment process and so bought into the company, that they made sure they improved and became the right candidate for the job next time? That’s a level of self-development and grit I would want in my team!
Allow time for innovation and creativity, it will be worth it
Most Data Scientists acknowledge that their role will sometimes involve the more mundane model maintenance and “plug and play” pre-existing models. To continue to motivate and inspire great Data Scientists, they must be given time to innovate, create and think about the bigger picture. By doing this, your organisation will not only benefit from a more productive employee who will be motivated to finish the more mundane tasks effectively and efficiently, it will benefit from insights and creative solutions of super smart people, that will forever improve your organisations.
Encourage continuous learning
Data Science is not a “set and forget” position. The landscape is ever changing and requires a commitment to continued learning to succeed. For a Data Scientist to remain great in their role, and keep your organisation ahead of the curve, they need to consistently be learning new methods, tools and techniques.
At the very least, to continue to get the best out of your Data Science team, you need to allow time for them to explore new ideas and continuously develop their skills. Even better if you have the resources to financially invest in these areas.
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