AI and data science are transitioning from theoretical concepts to tangible solutions for businesses of all sizes and industries. Consequently, companies are hiring data science product managers to manage the complex intersection of data, technology, and business.
To understand this pivotal role, we’ll explore:
- What is a data science product manager?
- The data science product manager career
- When a data science product manager is needed
What is a Data Science Product Manager?
The evolution of the product manager
The product manager role was born out of consumer package goods (CPG) companies that assign specific brand managers to oversee everyday products like laundry detergent, baby powder, or sodas. Within the past couple of decades, the software industry has adopted the role to guide the development and commercialization of software tools. As data science teams mature, organizations are also adopting product managers to develop and commercialize data-driven products.
Product manager domains
The modern product manager role sits at the intersection of various domains. Most commonly, the product manager role is positioned at the intersection of:
- Business: Product managers align product strategy with business goals. They understand market trends, financial considerations, and the competitive landscape. Product managers translate business objectives into a compelling vision and tangible product requirements.
- Technology: Product managers must possess a fundamental understanding of technology so that they can effectively communicate with engineers, analysts, and other product teams. This includes managing product and development life cycles like the software development life cycle or the data science life cycle.
- User Experience (UX): User experience is the cornerstone of many products. Product managers empathize with users, understand their needs and pain points, and be the “voice of the customer” in all relevant settings. They conduct user research, analyze user feedback, and design products that are intuitive to help solve customer pain points.
Data science product manager domains
Like their more general counterparts, a data science product manager also manages the business and technology domain. Some also manage UX. However, not all data science products have the UX component. For example, some data science products end with an analysis, a file delivery, or an API that feeds into another system.
As such, the data science product manager sits at the intersection of business, technology, and data. This replaces the UX domain with Data (see the top of the post for the Data Science Product Management Venn Diagram).
Indeed, data is core to all data science products. Thus, data science product managers understand data collection, basic statistics, data structures, and how to manage data as an asset. The most effective product managers also tell the story behind the data and the data science models to motivate relevant stakeholders to use or purchase the product.
The Data Science Product Manager Career
Data science product manager roles and responsibilities
A data science product manager’s roles and responsibilities vary widely. However, a data science product manager tends to be responsible to:
- Research data science industry trends
- Identify opportunities where advanced analytics, artificial intelligence, and machine learning can solve specific needs
- Develop a compelling product vision
- Set the product roadmap that clearly outlines the future releases and longer-term product strategy
- Prioritize work for the data science, data engineering, and advanced analytics teams
- Manage and articulate a product backlog of ideas
Career outlook
Regardless of the specific responsibilities, the data science product manager role is a challenging and highly rewarding career path. Career satisfaction is high because product managers get to drive the direction of a team and influence the cutting-edge strategy for an organization. Salaries typically start around $100,000 in the USA and new positions continue to open as companies invest in data, analytics, data science, and AI.
Indeed, the data science product manager sits at the intersection of two of the top ten roles in the USA:
| Rank | Role | Salary (Median Base) | Job Satisfaction | # Job Openings | Job Opening % Increase vs. 2021 |
|---|---|---|---|---|---|
| 3 | Data Scientist | $120,000 | 4.1/5 | 5,791 | +73% |
| 10 | Product Manager | $125,317 | 4.0/5 | 14,515 | +22% |
Related data product roles
In addition to strong career prospects, a data-savvy product major can specialize in different phases of the overall data life cycle. There is a strong overlap among these roles and the distinction is often blurred. However, generally, there are three other types of related data-focused product manager roles
- Data Product Manager: Drives the conversion of raw data into usable data sets. Their teams might develop usable data products for internal teams. They might also sell these data products to third parties for data augmentation. Often they enable work for downstream purposes such as analytics, data science, and AI teams.
- Analytics Product Manager: Manages the business intelligence and reporting layer on top of data. Likewise, these product managers might oversee the commercialization of SaaS products (such as Google or Adobe Analytics) or might manage reporting for internal company use.
- AI Product Manager: Manages the development and product operations for artificial intelligence systems. While the overlap in terms is fuzzy, AI tends to focus on creating intelligent systems, while data science focuses more on extracting knowledge from data. Like the other roles, some AI product managers focus on driving internal business outcomes, and others look to sell and commercialize their products.
Moreover, a product manager often serves in the Product Owner role which is a position specific to Scrum and Data Driven Scrum teams. The Scrum Guide defines this role to be “accountable for maximizing the value of the product resulting from the work of the Scrum Team”.
How to become a data science product manager
Regardless of your background, focus on further developing your capabilities across each of the key domains: business, technology, and data. Likely, you’ll need to shore up the weaker one or two domains so that you can grow as a well-balanced data science product manager.
Common Backgrounds
- Data Backgrounds: Some professionals grow up in a data analyst or scientist position (focused on the data domain) and learn competencies in technology and business to round out their product management capabilities.
- Software Product Background: Other data science product managers transfer over from software product management. These professionals often bring in a stronger business and technology base and need to skill up in data.
- Engineer Background: These professionals tend to have a strong base in technology and often shore up data and business skills.
- Others: And yet, some come from unexpected backgrounds. For example, I know a guy who was a theater major who transitioned into the career through an insensitive data science bootcamp, worked as an operations analyst, and self-studied cloud/information systems.
Data science product manager certification
To more fully develop your potential and to communicate your value to the market, earn a relevant product management certification:
- A Scrum product certification demonstrates your understanding of the Product Owner role and broader Agile principles. Three recognized certifications are the Certified Scrum Product Owner through the Scrum Alliance, the Professional Scrum Product Owner through scrum.org, and the Registered Product Owner through Scrum, Inc.
- For a more general background in product management, consider completing the Pragmatic Certified Product Manager course. It includes Agile practices but is more generalized than the previously listed Scrum certifications and focuses more on conceptual frameworks than the execution of a product owner role.
- We at the Data Science Process Alliance offer the Data Science Team Lead Plus course where you develop your competencies in data science product and project management. Unlike the prior courses, this course specifically focuses on data-driven products. The product content is in the Plus course which includes 4 hours of 1-on-1 coaching.
When a data science product manager is needed
To further understand the importance of this role, explore how an effective product manager is often the differentiator of product success and failure. To illustrate this, I’ll dive into a personal story (please learn from my mistakes!).
A failed project without a product manager
I worked on a project with clearly defined requirements to deliver a daily-generated forecast file and a user-facing dashboard. The team consisted of two data scientists, one data engineer, one Tableau developer, two subject matter experts, and me (the project manager). None of us truly researched the underlying business problem but proceeded to follow (somewhat blindly) the pre-defined requirements with some guidance from the subject matter experts.
Sound familiar to a project you might have worked on? If so, you won’t be surprised to learn that we missed a key unstated requirement that rendered the final solution useless. In short, we failed.
A successful replacement initiative with a product manager
So, we opened another initiative. This time – learning from my past mistake – I first developed a deep understanding of the fundamental business problem with the main stakeholder and his analyst team, identified the root issues, and then proposed some initial product features to fill both the stated and implied needs (see Data Science Minimal Viable Product).
In tandem, I worked with my data science development team to design a dynamic querying web app that generates on-demand forecasts based on the user’s filter selections. I had weekly conversations with the business team to iteratively define features and demo work completed.
After two months, my team delivered the initial product and we continued to launch new features for a few more months. By stepping in to research and understand the business needs and by defining and owning the product roadmap, I avoided the pitfalls of the first project to ensure this second one was a success.
This meme kind of summarizes my mistake
10 Signs You Need a Data Science Product Manager
More generally, there are many reasons why a data science product owner or manager is becoming more important for teams. Here are ten signs that you might need one.
1. The customer doesn’t understand what it needs
In the first project above, the business stakeholders defined requirements for a dashboard sitting on top of a forecast file when they really needed a dynamic querying and forecasting tool. Such problem-solution mismatch scenarios happen often because stakeholders tend to have only a partial view of data science (often involving a Tableau dashboard or some other tool they’re familiar with). Without a product manager to dig deeper and investigate what is truly needed, the ill-defined requirements might pass right on to the data scientist team who waste their and the stakeholders’ time building something that no one needs.
2. The customer expects a data science solution when it’s not feasible
Similarly, customers might think that a problem is solveable with machine learning when for some reason (e.g. lack of data, legal/ethics issues) it might not be. Or if ML can solve the problem, often a simpler analytics solution is all that is needed. This is where the product manager can step in and set expectations that perhaps the data science team should first explore but not commit to the requested solution.
3. The customer can’t identify good use cases
On the flip side, the business probably cannot identify the best use cases for data discovery, machine learning, and artificial intelligence. Someone who understands data science and can work with the stakeholders and data scientists to design a solution is much better equipped to identify use cases and deliver a data science product.
4. The customer lacks the time and the skillset
Managing a sizeable product can be a full-time job. If it is a side job for a business stakeholder (after all they have a business to run), the data science team may not get enough feedback or direction. Likewise, as illustrated in the prior Venn diagrams, product management is a profession that requires a broad range of skills that the customer might lack.
5. The customer doesn’t know how to use the results
The nuances of model interpretation are often not understood by the customer, and data scientists might not be able to effectively set expectations or explain why the most “accurate” model might not be the best. A product manager who understands data science can better facilitate communication among the data scientists and stakeholders and help the business understand and use the results.
6. The data scientists don’t understand the business need
A common flip-side of the above scenario is that the data scientists don’t understand the business need for the product. By translating the business needs into a language the data scientists can understand and by explaining the “why” behind a product, the product manager focuses the team on value-delivery. Consider using a data science user story format to communicate these needs.
7. No one is balancing competing demands
A common fallacy is to treat the executive sponsor as the only stakeholder. However, any sufficiently sized project has multiple stakeholders, each with competing demands. A good product manager understands these various competing demands, prioritizes development on the most important needs, and aligns the product with the broader organizational strategy.
8. Mis-timed launches cause problems
Kind of a sub-point of the above…If you wait for the best possible results, you’re probably delaying value delivery and stifling the product feedback loop. But if you deliver too early, you might adversely impact the business. Someone needs to make this call and it’s probably not the data scientists (who might want to perfect their work) nor the stakeholders (who might insist on a hard deadline for delivery that’s often too soon). Yet, an effective product manager can often guide an optimal timeline to deliver a series of small or low-accurate solutions first (perhaps for A / B testing) before the wider market launch.
9. Models need to be managed post-launch
Unlike traditional software that doesn’t need to be re-trained, data science products may often deviate from desired behavior over time. Someone needs to step in and manage the full product life cycle. A product manager can manage the bigger picture, even after the initial project has closed.
10. Maybe you should be developing a product suite
A data science team is often called on to solve a specific problem for a specific department. Sometimes this is what is needed. However, often the same work that is going into one data science product might also have another use case for a different (or even the same) department. Take for example, a customer churn model. Yes, the retention department might request this, but this same model can likewise serve product development, marketing, sales, and other teams. A good product manager looks across organizational and market needs and incorporates these broader needs into the product roadmap.
Conclusion
As data science continues to mature and become ever more integrated with operationalized systems, the role of the data science product manager is becoming more critical. It is a challenging yet rewarding and lucrative career path.
