The data science market continues to grow its demands for data science professionals who can solve business problems with their technical skills.
There’s really no typical candidate in the data science job market: they could be a fresh college graduate, a seasoned technical professional looking for a more challenging role, or someone who has taken a nontraditional path to study and learn data science skills later in their career.
In the hunt for data science talent, Adobe, a top global creative cloud and marketing technology company, works hard to create a culture where problem solving, continued education, and mentorship support their recruitment and retention efforts.
Read below to learn how Adobe approaches the data science field and gather insights about the job market from Mark Stevens, SPHR, director of talent acquisition at Adobe:
Stevens is focused on the selection and recruitment of specialized engineers, product leaders, scientists, and corporate professionals for Adobe. He has recruited both on the agency side and in-house at leading organizations.
Also read: Today’s Data Science Job Market
Data Science Careers Q&A
Working at Adobe
Datamation: What are your primary responsibilities in your current role?
Stevens: I lead talent acquisition and recruiting for our Cloud Technology business as well as our corporate functions, which enable Adobe’s 25,000+ employees worldwide to deliver their best work.
Datamation: What makes Adobe a unique place to work?
Stevens: Adobe continues to be a market leader, and we’re better positioned than any other company to capitalize on the fundamental shift towards digitization that’s happening across the globe today. Of course, with fast growth comes an array of new challenges and opportunities. To that end, I appreciate that Adobe creates a positive work environment where employees can drive their career growth and development. I have fantastic managers at Adobe who value my perspective, provide me with helpful feedback, and allow me to lead teams in my own best way. I work with exceptional colleagues day in and day out, and the overall rewards and benefits are hard to beat in the industry.
Datamation: What are the top three technical skill sets that your team looks for in new data talent?
Stevens: The skills we look for in our data scientists include:
- Foundational understanding of machine learning (ML) and statistics
- Foundational understanding of computer science, such as data structures, algorithms, space-time complexity, and more
- Knowledge of deep learning (DL) approaches for computer vision and natural language processing (NLP)
Datamation: What are the top character traits/soft skills that your team looks for in new data talent?
Stevens: At Adobe, we understand our future success depends on our ability to hire talented people with diverse perspectives. When we review candidates overall, we look for a values match in line with Adobe’s core values — genuine, exceptional, innovative, and involved — versus a pure capabilities and culture match. Philosophically, we also look for candidates with a growth mindset, who not only possess the skills needed for the present role but also have a passion to upskill and reskill as their job function evolves.
Strategies for getting hired in data science
Datamation: What do you think makes data professionals most successful in their roles?
Stevens: Besides focusing on the technical aspects, algorithms, or approaches, it’s key to understand the bigger business problems at hand. Successful data scientists have a solid understanding of business needs, the value that their solutions provide, and how users can ultimately leverage the solution. Secondly, knowing how to operationalize the machine learning model is important. We see many engineers who are keen to only work on the modeling aspect that they find interesting, but what they don’t realize is that operationalizing and maintaining models can take up significant effort and investment, without which the models are of no use. Data professionals can add tremendous impact on the business by also focusing on the operational aspects of optimizing data models.
Datamation: What is a unique expertise, certification, or other quality that sets data science candidates apart from the rest of the pack?
Stevens: The data science field is a fast-growing and ever-evolving one, and like any competitive career field, there are many ways to grow your skill set. For example, data professionals today can take advantage of Coursera, Udacity, and other online lectures and certification programs, not to mention certification courses that are offered via the more traditional educational route. Similarly for cloud computing, there are AWS, Azure, and Google Cloud Platform certifications. Data professionals can also set themselves apart by gaining a deep understanding of more advanced technologies, like Pytorch, Keras, and Tensorflow.
Learn more about the cloud job market: Cloud Computing Job Market
Trends in the data science job market
Datamation: How has the COVID-19 pandemic affected your approach to data talent recruitment and retention? How have you shifted your strategies to meet new job market demands?
Stevens: The global pandemic has fundamentally changed people’s expectations of the workforce, with more data professionals looking for flexibility in where and how they work. We have responded as a company by offering employees flexible schedules and remote work options for certain roles, while generally embracing a more hybrid work mindset that combines in-office and remote work. Additionally, to help employees better acclimate to the challenges of blending work and life, we introduced several new health and wellness benefits, including monthly company-wide days off and additional time off for employees directly impacted by COVID-19, amongst others.
Datamation: How have you seen the data science job market change since you first started? How have the technologies, services, conversations, and people changed over time?
Stevens: I’ve been working with and recruiting for data professionals for over a decade now, but the market has changed more in the last two years than ever before. Even in the Bay Area, where we can expect the highest density of available talent, hiring has become more competitive. As an example, recent advances in artificial intelligence (AI) and machine learning have created a larger demand for data science talent. New innovative fields such as self-driving cars, robotics — areas such as industry automation, drones, food delivery, warehouse automation, air taxi, and more have further increased demand for data scientists.
Also read: Data Science Job Market Trends
Data science recruitment, retention, and new opportunities
Datamation: How does your team provide professional growth opportunities and other benefits that improve data professional talent retention?
Stevens: To support Adobe employees in their pursuit of continuing education, we provide several education and professional development reimbursement programs. For example, employees will be reimbursed up to $10,000 per year for academic degrees, top credential programs, advanced specializations, and technical certifications. There is also a $1,000 yearly fund for short-term learning opportunities, like attending conferences, webinars, and taking online courses.
Securing patents or being published in reputable publications are also actively encouraged, and there are often financial incentives for those who pursue them at Adobe. We have a $7,200 bonus for patent inventors, and a $1,000 bonus for authors of a published paper.
Within my team, I encourage everyone to explore new projects and to produce unique solutions to business problems. And as a company, Adobe leaders do their best to understand the passions and strengths of their team members to help them pursue growth in areas that matter the most to them.
Datamation: What advice would you give to a longtime data professional who wants to move up or find a better career opportunity?
Stevens: I have a couple of points of advice for data professionals or any tech professional who’s looking for new opportunities:
- Love what you do. If you don’t like what you are doing, you will not be able to excel.
- Focus on where you could add the most value to the organization.
- Take ownership and drive your career. Determine what you want to do and where you want your career to go, then work with your manager and company leaders to make that happen.
- Be a mentor and get a mentor.
Build your skills in data science: 10 Top Data Science Certifications
Datamation: What do you consider the best part of your workday or workweek?
Stevens: Two things:
- Mentoring my team members and helping them grow their careers at Adobe.
- Reviewing our wins of the day, week, or quarter and seeing all the positive, impactful contributions my team made toward the success and growth of the business.
Datamation: What do you like to do in your free time outside of work?
Stevens: I’m very involved in my kids’ extracurricular activities, like coaching their sports teams. Being based in Utah, we have a propensity for outdoor activities, such as hiking, snowshoeing, fishing, camping, riding ATVs, and more.
Look for a data science job today: Top 50 Companies Hiring for Data Science Roles