1. Think long term during the hiring process
In this Data Science frenzy, it is easy and tempting to hire what I call “warm bodies” – resources that will “get the job done”.
Hiring advanced analytics talent is an investment, not only in monetary terms but it is also an investment in your organization’s future.
Every assignment or project they work on, they gain skills and knowledge that is an investment your organization is making. Make sure that you keep retention on the top of your mind during the hiring phase:
(1) Be honest in job description about the role and responsibilities so that there are no surprises for the candidate after they come on board.
(2) Make sure you provided candid inputs to the candidate on the company culture so they don’t feel like an outlier after coming on board.
(3) If you think there will be “difficult” stakeholders that this role will have to interact with, don’t shield the candidate from these individuals during the interview process. Candidates need to be familiar with all types of stakeholders they need to interact with.
(4) During onsite interviews, give them the “feel” of their would be workspace and the workplace infrastructure and amenities.
You will be surprised how many data scientists I have interacted with leave because they did not like their “surrounding” after they started.
2. Provide structured onboarding
After the candidate has been extended an offer, this is the most critical aspect from my perspective.
(1) DO NOT combine first week of onboarding of your data science resources with other new hires (other than the sessions where new hires go through standard policies and paperwork). Customize the first week so that it is focused on them.
(2) Develop a detailed plan for first 90 days. Don’t rush to make them work 100% on projects as soon as they start, even if they are senior hires. Integrate a daily onboarding plan like meeting key stakeholders etc. in their schedule for first three months.
(3) Provide the onboarding plan to the candidate after an offer has been extended and before they start and seek their feedback to determine what they think is critical for them to be successful is missing.
3. Tailor their engagements better
You may not have the privilege of having a large Data Science but irrespective of the size of your Data Science team, try your best to align the projects you assign to your resources that aligns with their expertise and interests. They need to play to their strengths but also need to expand their knowledge zone. It is a delicate balance but is necessary to take into account for retention purposes.
4. Invest in continuous learning
World class Data Scientists understand that the field of Advanced Analytics and Data Science is exploding and the only way to stay relevant is to learn continuously, off the job as well (i.e while not working on actual projects). Invest in their continuous learning. As much as possible, don’t throw “cookie cutter” curriculums and trainings at them.
Each Data Scientist, over their career, develop certain areas of expertise and areas of interest. Forcing them to undergo training that primarily falls in their area of expertise but not in the area of interest that they want to develop, will make the training a painful “checkbox” exercise.
One of my friends once worked for an employer who would force her to undergo standard curriculum training in areas she was already familiar with. When she insisted that she does not think that training will be useful, they labeled her as someone who is not “open” to learning new things and an individual who is not self aware about development opportunities. This is a sure shot recipe for attrition-please try to avoid this.
5. Provide them ample career opportunities
Creating multiple hierarchies in Data Science teams is difficult and not always feasible. Even though you create hierarchies like Data Scientist > Senior Data Scientist > Lead > Principal etc., the talent in such teams is generally great so promotion decisions are not only difficult, they can be a reason for attrition as well.
However organizations don’t realize that their Data Science teams are not just a pool of Advanced Analytics resources.
Imagine if you can find a Data Scientist from that team who can work at the intersection of Data, Technology and Business. Making them lead functional planning departments after they have spent few years as Data Scientists will not only be a career enhancing opportunity for them but can do wonders for your organization by helping propagate a data driven culture in the organization.
This is just an example but if you can get creative and create multiple career paths for your Data Scientists, you will be able to keep that knowledge within your organization.
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