Avatar By: Matt Cowell

Employee Retention Strategies for Young Data Talent

Employee retention strategies are top priority for Human Resources these days.  HR managers have been telling us lately that one of the biggest problems they are trying to solve is that of young employee retention.

In data science and analytics, this problem is especially troubling due to the ever-increasing job opportunities and salaries available to bright young people who have top data and analytics skills.  Hiring data science team members is tough.  Retaining them is even tougher. But retaining young analytics team members could be the toughest job of all for HR right now.

Why? Young data science and analytics talent comes with its own unique set of challenges:

  • Due to the plethora of new data science undergraduate programs, junior hires may come with less real-world work experience than “traditional” Data Scientists.  So, they may be planning to test the waters in a few different jobs.

    The plethora of young data science graduates has different experience and expectations
  • Trends show that young people are moving away from humanities and social sciences majors in favor of math/statistics and STEM majors to keep up with the job market demands. If these young employees are not naturally inclined towards data related jobs, they may be a little more difficult to retain.
  • Data Scientists are a particular type of personality that seek intellectual challenge and variety in spades.
  • These bright, motivated personalities may not always possess strong soft skills or have experience communicating with business people. This can isolate them from the broader organization.

On top of these challenges, there’s a multitude of factors unique to the Millennial and Gen Z generations that require HR  to create employee retention strategies that address the younger data science crowd.

We’ve put together our list of reasons why we think it’s hard to retain young data talent. This list is based on our conversations with analytics leaders, recruiters and HR Managers and from feedback we get at HR conferences.

The first four reasons we list are primarily due to the changing nature of the young workforce.  The last three have to do with younger employees’ overwhelming desire for growth opportunities in the workplace.  Later we suggest a few employee retention strategies to address young data science talent.

Mismatched Expectations

With all the datathons, hackathons and Kaggle competitions around, young people may come into a data science role thinking it will be like an exciting data competition. Nothing is further from the truth. Young data science employees may not fully grasp what it means to work with software and data engineers on a larger team to deploy data science models and projects. This can create some uncertainty regarding their role.

In addition, we’ve all heard that Data Scientists are generally dissatisfied with the amount of cleaning, wrangling and collecting data they need to do to get their job done.   It may fall on the younger team members to do this mundane work. This of course can be demotivating. Worse, there’s no prize or reward for doing so.

Poor Technical Managers

“Great data scientists have career options and won’t abide bad managers for very long. If you want to retain great data scientists you’d better commit to being a great manager.” – Harvard Business Review

Long gone are the days when a bright junior employee would stick out a role with a bad manager in the hopes of getting promoted to a better job someday.  Alas, many analytics team managers reach senior management level because they were great individual contributors or have the Ph.D. to “prove” they can do the job.

However, strong technical performance does not always translate into strong managerial performance.  Being a Data Scientist is stressful enough, with many long hours.  The analytics team is often exposed to unreasonable demands or expectations from the rest of the business, or worse, unappreciated for their contributions.  They are often tasked with critical problem-solving.

Because of this analytics managers need to support and insulate the youngest team members from this kind of stress or a poor work environment. An unsupportive or demanding manager is sure to send a few smart young guns running.

Lack of Mentors

“If you don’t mentor your millennials they will leave you.” – Julie Kantor, chief executive of training and development firm Twomentor.

Every year the Deloitte Millenial survey provides tips on how to retain Millenials

Deloitte found in its 2018 survey of Millennials that those who intend to stay more than 5 years with their company are twice as likely to have a mentor than those who do not intend to stay long.  Another survey by Qualtrics found that most Millennials were willing to give up as much as 12% of their salary for a management structure that emphasizes mentorship.

A lack of mentors is clearly something young people expect these days and is a barrier to young employee retention.  Mentoring is crucial to young Data Scientists in order to bridge their knowledge gaps and improve their understanding of and connection to the business.  These new hires want to excel in their jobs and they see mentoring as one way to do so.

Most new data science team members are unclear about the roles and skills expected of them. Mentors can guide young team members on how and where they can fit into the data science team and how their analytics efforts will contribute to its results. They also play a crucial role in feedback, which brings us to our next point.

Lack of Feedback

A big plus of mentorship is feedback.  Feedback is part of developing, growing and learning, something most young employees crave.  Data Scientists are hardworking people.  If they’re slugging it away wrangling data and getting models into production but never get the input from a good tech manager or mentor of a job well done or pointers for improving their skills, they’ll become disengaged.

You see, young data science team members are used to turning in projects at university and getting frequent feedback from professors.  They also come from a generation that received constant praise and was always told to “do their best” and that was good enough.  They’re often intellectually competitive too.  Then they come to work, create models and get no feedback or input on performance. It’s incredibly frustrating to them.

It’s also worth remembering that the younger generation are some of the most indebted university grads in history.  Without feedback, they have no way of knowing if they are doing a good job.  So in the absence of feedback, they’ll assume the worst and think they are soon to be out of a job. They’ll start looking to jump ship – because as Data Scientists they easily can do this before that ever happens.

No Job Flexibility

In a Jive Communications survey of Millennials, 37% said that having a job with flexible hours is “essential”.  25% had left jobs because they couldn’t work flexibly.  Data Science team members work long hours and work hard at getting a model to work correctly and later monitoring it and tweaking it for performance.  They spend a lot of time dealing with uncertainty and learning new tech tools. The job is intense.

Young employees expect that if they work this hard, that there will be some perks in the form of flexible working situations to help them achieve the important work-life balance that they crave much more than previous generations.

Lack of Upskilling, Learning, and Development

Several studies show that millennial employees say that more training and developmental opportunities at work would keep them from leaving their current jobs to pursue opportunities elsewhere. Put simply, younger employees don’t want to stay in a company or team where they see no opportunities for growth.

There are many factors that can lead someone to perceive no room for growth. A lack of learning and development opportunities is arguably the biggest one. In fact, it’s a problem that impacts just about every industry.

The Deloitte Millennials survey also found that “Respondents lack confidence that they can succeed in an Industry 4.0 environment and are looking to businesses to help them develop the necessary skills, including the “soft” skills they believe will be more important as jobs evolve.”

Data science is a field at the forefront of Industry 4.0. It’s booming with new methodologies, technologies, and applications to explore every day. As such an essential employee retention strategy is to provide a progressive learning environment for young Data Scientists.  Young people are especially hungry to learn new things.  If a young Data Scientist wants to explore a field beyond their current scope of work, say NLP, and they see no opportunities to do so, this can lead to attrition.

Not Enough Challenge

Growth also comes through overcoming challenges.  A lack of challenging projects has always been a retention issue for data talent, both old and young. It’s particularly troubling for Millennials and Gen Z. These generations expect to be exposed to new tools and technologies, after all they grew up on tech. They have the sense as a generation that there is always something new and shiny around the corner. So there’s no way they will enjoy building and reiterating on the same models year after year.

Particularly in companies that are relatively new to Data Science and analytics, much of a Data Scientist’s time may be spent dealing with poor data quality and access or justifying outcomes to seasoned but not analytically savvy business people who just don’t get it.  After a certain amount of time bumping against these walls, young people will simply take their ideas and talents somewhere else.

No Clear Career Advancement Path

Despite the rumors to the contrary, HR managers are convinced that many Millennials they hire do start a job looking to grow their careers in the company and are not actively seeking to quit after just a few years of employment.  A survey from Instructure back this up, finding that 90% of millennial employees are looking to grow their careers within their current companies.

That said, young employees are more restless than their predecessors and churn rates are high. If they don’t see promotion opportunities within months of starting a new role, they’ll be thinking about leaving. And because Baby Boomers are rapidly retiring and there aren’t enough Gen Xers to fill the vacant senior roles, younger employees are jumping at the chance to get hired into other roles that are beyond their experience level.

7 Employee Retention Strategies for Young Data Talent

If any of the above issues sound like young employee retention barriers that your company is facing, then you’ve got work to do to retain your youngest Data Scientists and analysts.  We’ve come up with 7 employee retention strategies:

1. Improve your onboarding communication and process

Have the analytics team leader lay out very clearly where a young employee will start, what they will be doing and how they fit into the team and the broader organization. Communicate to junior hires what the options are for advancement and what milestones and skills they will need to develop to get there. Then tell them about the mentoring, learning and development and personal growth opportunities that can help them achieve their advancement goals.  Be sure too, to communicate any uncertainties upfront, such as a lack of business buy-in or ongoing data management issues, and manage expectations and leave the door open for discussion about these.

2. Create a specific learning and development program for young data scientists

While a nice perk, Udemy for Business is just the start to data science learning and development needs

Learning and development should not consist only of handing out subscriptions to Udemy for Business – although this perk is much appreciated by young employees.  Young curious data science talent wants a more personalized and obvious skill growth and career development program.  This includes personalized assessments (we can help with that!) and career planning.

Development programs should also include deliberate exposure to a variety of data science projects, platforms and business interactions. This will also help address the need for challenge and purpose. Don’t forget that mentoring and coaching should be part of L&D path.

3. Implement a formal mentoring, coaching or sponsorship program for young data science team members

Mentoring and coaching will play a big role in providing a personalized career development path. It will also provide that informal two-way feedback that young team members crave and which can help analytics managers spot talent that is at risk of leaving.  Within the mentoring or coaching program, it’s a good idea to establish communication with members of the business team as well, perhaps by assigning a business sponsor. This will help new Data Scientists to gain knowledge from business leaders and improve communication.

4. Implement more frequent feedback mechanisms

It’s an important employee retention strategy for Millennials and Gen Z that feedback is more frequent and also two way. Two-way feedback helps to mitigate the “poor technical manager” challenge too.  Millennials can also be given a chance to ask questions and ask for things they need to stay happy on the job or opportunities to provide input that makes them feel like a valuable part of the team.

It’s a popular idea these days too to create a “reciprocal” mentoring program where young employees counsel older ones in certain areas, usually tech, analytics and digitalization, that “Boomers” may not quite grasp.

Lastly, pulse surveys should be implemented to find out things such as what young employees’ preferred methods of communication are, what they envisage their career trajectory being in the company, or how they feel about team culture, impact and purpose.

5. Offer flexible working options

The writing is on the wall. If you haven’t adopted a flexible working program or begun to engage with the gig economy, then you will always be struggling to retain tech talent.  Workers of the younger generations are at different stages of their lives and want different things from their careers. HR teams should empathize with this and accommodate employees where possible.

That said, flexible working options must not be seen by HR as “part-time” work. These options must offer the same opportunities for growth, feedback and recognition as “desk jobs”. The focus should be on the results employees produce rather than how they get there. By adopting this mindset, flexible working options can be a powerful employee retention strategy.

6. Develop young data science talent internally

You may wonder at first what this strategy would have to do with retaining young employees, but we think it’s probably the most effective retention technique.  Rather than hiring fresh data scientists from the outside who lack loyalty and are squirrely, identify young talent internally that can be developed into data talent.   Look for people with high IQs, who consistently meet or exceed goals, or who have quantitative backgrounds. Chances are outside of your data science team, there’s also a host of young people looking for new challenges and career advancement opportunities.

7. Institutionalize a career path for young data professionals

Meaningful work and career opportunities are critical for engaging and retaining all types of employees. Data Scientists are no exception. To address the issue of a lack of perceived growth opportunities, a specific set of data science and analytics career paths should be developed in your company. 

Several options should be developed in roles for data science, data engineer, or business analytics consultancy, for example. It’s important to include opportunities in the business as well, such as in marketing, operations or general management.  You should also create clear steps and ways to transition between career options. And remember, if a rock start young analyst has management aspirations, make sure that they are well trained in management and mentoring as they rise to the top!

You can also tie data science career advancement in with learning and development by incorporating requirements such as obtaining certain certifications, participating in specific types of projects and demonstrating certain skills. This will help your overall retention strategy of supplying ample growth opportunities.

Employee Retention Strategies – Summary

Rather than fretting about how to retain young employees, HR and analytics managers should sit down with their younger team members to find out what each employee is looking for in terms of growth, challenge, and advancement.  This should be done through frequent feedback and mentoring sessions.  In these sessions a clear path for learning, development and career advancement should be laid out and discussed with younger employees.

Then it’s a question of executing these employee retention strategies and improving policies such as job flexibility options, internal hiring and training, and onboarding over time to get it right, and hopefully lay down the path for long term retention of promising young data talent.

 

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