Data science’s impact on businesses is far-reaching. From crafting marketing campaigns and blocking cyberattacks to increasing efficiency and reducing costs, the field is vital to business success. Playing a key role in risk management, stock trading, target advertising and more, data scientists perform predictive modeling and categorization and develop technologies such as recommendation engines. They are also key players in promoting an organization’s ethical and social responsibilities, working to dismantle biases in data.
Data science managers lead teams of data scientists and help shape an organization’s approach to data. Leadership skills, a background in data analysis and structure, and the drive to help create a data-informed company are three important aspects of the role. The path to this key position includes education, training, experience, and developing both learned and natural skills, such as machine learning and team-building.
Through defining product success, setting goals and creating processes, a data science manager tracks their team’s impact on its organization. From there, they work toward maximizing that impact, focusing on data areas that affect the company to a greater extent. This process is the building block of a successful organization, helping inform decisions and establish company growth.
Finding and setting achievable goals is a key element of a data science manager’s role. For example, a goal might be to create customer profiles to influence a company’s next targeted marketing campaign.
Having set this goal, the data science manager defines its desired impact — such as increasing sales — and then focuses the team’s approach, creating a process that might include correctly mining customer data, formatting it in a digestible manner for those outside the team and presenting data to high-level executives.
While this example involves marketing, data science managers also help drive an organization’s ethical and social responsibility, using the same process to confront biases and equitably analyze data.
Team management includes coaching, maintaining a positive work environment, assisting with roadblocks and helping to prioritize projects. Key to successful management is creating a trusting and candid environment, ensuring that team members know the value they bring to the team, offering direct feedback to each team member, and receiving and acting on members’ feedback.
Another key aspect involves team-building. Data science managers hire data engineers, analysts, visualization developers, translators and architects. In fulfilling this responsibility, process managers should aim to create diverse teams, including applicants with different backgrounds, areas of study and previous job experiences. Looking at a team’s makeup and identifying gaps is an important part of the manager’s role.
Building a representative team and investing in talent can maximize data science’s impact within the organization, helping business professionals make informed decisions, creating actionable datasets and offering further insight into organizational structures’ efficiency.
Education, training, experience and specific technical skills are crucial to success as a data science manager. As such, steps toward a management position in this field include earning an undergraduate degree, gaining experience in data science, earning additional certificates and learning how to be an effective leader.
The path toward a career as a data science manager begins with earning an undergraduate degree with a relevant major such as statistics, computer science or information systems. Key subjects of expertise for data science jobs include data structures, data mining and security applications.Those planning to advance from data scientist to data science manager would benefit from earning a master’s degree. Information management graduate programs, for example, can help professionals develop the leadership and technical skills required of data science managers.
Additional training can also improve a data scientist’s chances of securing a management position. Professional certificate programs demonstrate both mastery of skills and commitment to the discipline. Earning a credential such as Microsoft Certified Professional or Certified Analytics Professional, which requires passing an exam, can help open the door to a management-level career.
Aspiring data science managers should gain several years’ experience as data scientists or analysts. This builds foundational skills and teaches the dynamics of working on a data science team. Typical career paths for data science managers begin in jobs such as data engineering, product management or data analysis. Experience using programming languages such as Python, R and SQL to code and manage databases, analyzing data, and presenting findings to decision-makers is invaluable for aspiring data managers.
To be successful, data science managers should exhibit skills ranging from technical knowledge to the ability to gain trust. Technical skills in statistics, data analysis, data visualization and machine learning help managers extract data, transform it into insights and then convey their findings to other members of the organization.
A data science manager must also have strong team-building and leadership skills. These allow them to earn respect, create team structure, build trust and connect their work to the work of others in the business.
With the increasing reliance on big data to drive business decisions, data science managers are in high demand. The U.S. Bureau of Labor Statistics (BLS), which groups data science managers with computer and information systems managers, predicts the field will grow 10% between 2019–2029 — much faster than the job market as a whole. According to PayScale, data science managers made a median salary of approximately $137,000 as of January 2019.
Earning the right graduate degree is a step toward a rewarding career as a data science manager. The University of Washington’s Master of Science in Information Management program offered online teaches students how to transform data into organizational insights. This practice includes identifying and implementing equitable information management strategies. Learn more about how the University of Washington can start you on the path to a new career as a data science manager.