the twin career paths of Data Scientists
... and why a career as an IC or manager should be available to you
TL;DR
Data Scientists have two distinct career paths in a Data Science Shop: Individual Contributors and Managers.
Individual Contributors focus on technical tasks, while Managers balance technical and administrative tasks.
Junior Data Scientist managers balance technical and administrative tasks, focusing on tactical aspects while Senior Data Scientist managers lean more towards administrative and strategic responsibilities, with less hands-on technical work.
Avoid two blunders: (1) forcing the management track on every IC, create separate career paths for each role and let them choose; (2) managers who spend most of their time in the technical weeds are flagging that they were not ready for a management role.
For the past decade, I've had a front row seat to the evolving Data Science landscape. One particularly fascinating change has flown under the radar: the emergence of a fork in the Data Scientist career path. This split gave birth to a new role that I call the Data Scientist Manager – think Data Scientist + Product Manager + Team Leader + Strategist – that's reshaping how Data Science Shops operate.
I have closely seen how this trend took hold in Data Science Shops outside of Big Tech, and it’s interesting that a similar phenomenon has happened in Big Tech too!
The Data Scientist Manager became a natural path for professionals who can bridge the gap between technical Data Science and broader business objectives, and who are keen to ensure that their Data Science Shops build truly impactful Data Products.
To be clear, any Data Scientist in a contemporary Data Science Shop has two career paths available: as an Individual Contributor (IC) or as a Manager. Both start their careers as highly technical Data Science professionals, but their paths diverge based on their inclinations and the needs of the organization.
Who are the Data Scientist Individual Contributors?
Data Scientist Individual Contributors (ICs) are the technical backbone of any Data Science Shop. These professionals are characterized by their deep expertise in the core technical aspects of Data Science. Typically, they come from strong academic backgrounds, often holding PhDs or other advanced degrees. Many transition from academia to industry, attracted by the intellectual challenge of solving complex problems and are often most comfortable when deeply immersed in code, algorithms, and computers.
Think of a Data Scientist IC as a Yo-Yo Ma of Data Science. Just as Yo-Yo Ma is a virtuoso cellist who has spent decades honing his craft, a top-tier Data Science IC is a master of their domain, constantly refining their skills. Like Ma, who can coax soul-stirring music from his cello, a skilled Data Science IC can transform raw data with sophisticated algorithms, creating “music” that speaks to both technical and business audiences.
Yo-Yo Ma doesn't aspire to conduct the entire orchestra or manage the symphony's operations. Instead, he channels his passion into pushing the boundaries of what's possible with his instrument, collaborating with other musicians, and delivering breathtaking performances. Similarly, a Data Science IC thrives on diving deep into complex problems, developing cutting-edge algorithms, and producing work that advances the entire field.
For that reason, the Data Scientist IC path is ideal for professionals who are deeply passionate about the technical aspects of Data Science and derive satisfaction from hands-on work with data, algorithms, and computers. This path suits those who thrive on solving complex problems, enjoy the intellectual challenge of developing innovative solutions, and pushing the boundaries of what's possible with data and algorithms.
Who are the Data Scientist Managers?
Data Scientist managers emerged organically from the ranks of ICs. They were ICs who recognized that successful Data Science extends beyond technical expertise. They understood the critical need to bridge non-technical gaps and ensure the effective implementation and adoption of Data Products.
Think of a Data Scientist manager as a Pep Guardiola of Data Science. Guardiola, the soccer mastermind behind Manchester City’s success, was once a world-class midfielder, much like how many Data Scientist managers start as high-performing Data Scientist ICs. Like Guardiola, who brought his experience as a player and deep technical understanding of the game into his strategic leadership of Man City, Bayern Munich and Barça, a Data Scientist manager fuses technical expertise and business strategy, leading Data Science Shop crews to build Data Products that drive organizational success.
Guardiola doesn’t need to be the best player on the field anymore – that’s not his job. His role is to see the bigger picture, to strategize, to bring out the best in his team, and to make crucial technical decisions that win matches and championships. He still knows the technical aspects inside out, which allows him to communicate effectively with his players and make informed tactical choices. That’s your ideal Data Science manager – someone who can leverage their technical background to lead, strategize, and elevate their entire team's performance, all while keeping their eyes on the ultimate prize: business impact.
The path of a Data Scientist manager is ideal for Data Scientists who are drawn to the strategic aspects of Data Science and who aspire to shape the direction of Data Products within their organizations. Data Scientist managers must adeptly navigate both the technical intricacies of Data Science and the complex landscape of organizational strategy and stakeholder management, making them invaluable assets in translating Data Science potential into tangible business impact.
What essential tasks distinguish a Data Scientist manager?
While maintaining their technical proficiency, Data Scientist managers take on responsibilities that set them apart from ICs. They coordinate the work of other ICs, handle administrative tasks, and serve as a vital bridge between the Data Science Shop and other areas of a company. This expanded role allows them to align technical capabilities with business objectives more effectively because they can conceptualize straightforward solutions, understanding what is technically feasible while addressing real business needs and challenges.
Specifically, five general areas set Data Scientist managers apart from ICs. From nurturing talent to navigating organizational dynamics, these tasks highlight the unique value that Data Science managers bring to the Data Science ecosystem.
1. Team leadership, development & administration
Hiring, mentoring, and managing the Data Science Shop crew
Handling team-related administrative tasks (e.g. budgeting, resource allocation, performance reviews)
Fostering a collaborative and innovative team culture
2. Technical guidance & oversight
Ensuring quality and innovation in technical solutions
Guiding technical decisions and approach
Staying current with emerging trends and technologies in Data Science
Balancing innovation with practical business needs
3. Strategic alignment & opportunity identification
Aligning Data Science initiatives with company goals
Identifying new opportunities for Data Products
Developing long-term strategies for using Data Products
Collaborating with other departments to integrate Data Science across the organization
4. Project coordination & execution
Managing the development of the Data Product Cycle
Balancing resources and priorities across projects
Ensuring timely delivery and successful implementation of Data Products
Monitoring project progress and mitigating risks
5. Stakeholder management & communication
Translating between technical and business languages
Building relationships across the organization
Presenting Data Science insights and recommendations to leadership
Advocating for Data Science resources and initiatives
A career progression for Data Scientist managers
Data Scientist managers follow a career journey that balances technical expertise with growing leadership and strategic responsibilities.
As junior managers, Data Scientist managers often start with one foot still firmly planted in the technical realm. They lead small teams or specific projects, providing hands-on technical guidance while also beginning to handle administrative tasks and stakeholder communications. At this stage, they're learning to translate complex technical concepts for non-technical audiences and starting to align their team’s work with broader business objectives. Junior managers typically spend a significant portion of their time on tactical decisions, ensuring the day-to-day progress of projects and the professional development of their team members.
As Data Scientist managers progress to more senior roles, their focus shifts increasingly towards strategic leadership and organizational impact. Senior managers oversee larger teams or entire Data Science Shops, making high-level decisions about resource allocation, project prioritization, and long-term strategy. They spend less time on hands-on technical work and more on fostering innovation, ensuring best practices across multiple projects, and advocating for Data Science at the highest levels of the organization.
The deep technical background of senior Data Scientist managers becomes a foundation for strategic decision-making, allowing them to identify large-scale opportunities for Data Science across the organization and align these initiatives with company-wide goals. Senior managers also play a crucial role in shaping the data culture of their organizations, building relationships across departments, and presenting strategic insights to C-suite executives. This progression reflects a gradual shift from tactical, team-focused leadership to broader, more strategic organizational influence.
Avoid these blunders at all costs!
We have unpacked the IC vs manager career paths for Data Scientists. Now it’s time to shine a light on two colossal blunders that can – and will! – throw a wrench in your Data Science Shop’s gears.
First, the “accidental manager” syndrome is still sadly a common occurrence. Stepping into the manager track is not for every IC. Yet, many Data Science Shops insist on “promoting” their star ICs into management roles as the “natural” way to advance their careers. Forcing an IC who’s a happier algorithm whisperer into a management role is a surefire way to lose a great IC and gain a miserable manager. It’s much better to let Data Scientists choose their own adventure – IC or manager – and watch them thrive.
The skills and personality traits that make someone an excellent IC in Data Science are often quite different from those required to be an effective manager. Many ICs excel in their roles because they love the deep technical work, the challenge of solving complex analytical problems, and the opportunity to continually advance their expertise in specific areas of Data Science. These individuals may find management responsibilities less fulfilling or even frustrating. Forcing such individuals into management roles not only potentially reduces their job satisfaction but also deprives the organization of their valuable technical contributions.
Moreover, effective management in Data Science requires a unique blend of technical knowledge, leadership skills, business acumen, and strategic thinking. Not all ICs possess or desire to develop these additional competencies. A great Data Scientist IC may not necessarily have the communication skills, people skills, or big-picture thinking required to lead a team or interface with non-technical stakeholders. By promoting ICs to management indiscriminately, organizations risk creating ineffective managers and simultaneously losing strong technical contributors.
Second, the “can’t-let-go manager” syndrome is a bit sneakier but just as problematic. This is the senior Data Science manager who remains elbow-deep in code and algorithms and treating leadership duties like a side gig. It is true that when extraordinary situations call for it, all hands – including senior managers – roll up their sleeves and do technical work. However, if this is part of a manger’s daily routine, it’s a misuse of a manager’s time and expertise. Constantly engaging in hands-on technical work prevents managers from focusing on their critical leadership responsibilities, ultimately hindering the team’s overall effectiveness and strategic direction.
A Data Scientist manager stuck in the technical weeds is missing the forest for the trees - and probably dropping the ball on actual managerial duties. It's like having a head chef who spends all their time chopping vegetables – important work, but not what they’re being paid to do. Remember, the superpower of a Data Scientist manager should be amplifying the team's impact, not hogging the technical spotlight. This kind of behavior might be a red flag that this manager never really transitioned from IC to leader.
A simple solution: Smart Data Science Shops provide distinct career paths for both Data Scientist ICs and managers, understanding that not every IC should or wants to become a manager. As ICs advance, they should be given opportunities to take on more complex technical responsibilities and deepen their expertise. Similarly, managers should transition towards more strategic roles as they become more senior, gradually shifting away from tactical responsibilities. This dual-track system allows for the development of both technical wizards and strategic leaders, each with their own set of hard and soft skills, expertise, and career milestones. By doing so, organizations can nurture and retain top talent in both tracks, ensuring they have the right people in the right roles to drive Data Science innovation and business impact. Right people, right roles, right results!
The twin paths: a healthy development for Data Science
The emergence of twin career paths for Data Scientists reflects the field’s maturation and growing complexity. As organizations increasingly leverage data for decision-making, they require both deep technical expertise and strategic leadership. This evolution addresses the need for professionals who can not only solve complex technical problems but also align Data Science efforts with broader business objectives, effectively bridging the gap between technical innovation and organizational strategy.
Did we spark your interest? Then also read:
WHO are the people that make Data Science possible? to learn more about the technical roles that collaborate to build Data Products in a Data Science Shop
What is the Data Science Shop? to learn more about the roadmap for the operation of Data Science in a business
Data Science Shop: why this Project? to learn more about the story behind the creation of the Data Science Shop