How To Become a Data Science Product Manager

Product management is like running experiments, using data to validate decisions, and continually learning. Although the product manager role originated in consumer packaged goods companies, where brand managers oversaw physical products, it has been adopted by various industries in recent years.

As a data science product manager (DSPM), you’ll prioritize problems, manage stakeholders, and collaborate with engineering and design teams to develop solutions.

Despite the lucrative base salary of $160,000 and the tech and finance industries rapidly hiring PMs, the role can be pretty stressful because of the responsibilities you’ll be under. For example, we are seeing an uptrend in job postings for data science product managers from companies like Capital One and PepsiCo. To succeed in these roles, you must be passionate about your product area and embrace rapid learning and adaptability.

In How to Become a Data Science Product Manager, we’ll guide you through the steps you can take to become a data science product manager—with or without experience.

If you’re already pursuing a career in data science, transitioning to a role as a data science product manager becomes less challenging. Here are the steps to becoming a DSPM with industry experience in data science.

Getting Certified as a Technical Product Manager

A technical product manager certification is the first step toward becoming a DSPM. Most certifications, including CSPO, PSPO, NPDP, CIL, etc., include fundamentals of product development, driving innovation, and handling product lifestyle modeling.

This certification validates your understanding of technical product management principles and your ability to bridge the gap between technical teams and business stakeholders. It also demonstrates your proficiency in product strategy, roadmap planning, and technical requirements gathering.

Mastering Product Management Fundamentals

Developing skills beyond the technical aspects of product management is the next step in excelling as a DSPM. Core product and data management skills, including analyzing data trends, anticipating customer behavior, and managing cross-functional teams, will be critical in your data science product management role. Moreover, since you will oversee the entire product lifecycle from ideation to launch, you need to learn to align products with business objectives.

Your leadership, communication, and organizational skills will take priority over your data science prowess in DSPM roles. That’s why you can’t expect to apply your data science skills directly to product manager responsibilities without proper training and preparation.

Earning a Data Science Certification

Even if you’re transitioning from a data career, handling data-driven products requires an expert-level understanding of data science. MBAs aren’t prioritized in data science product manager roles. Rather, companies hiring for DSPM roles focus on your understanding of data science methods, machine learning algorithms, and product sense problems.

Obtaining certification in data science can help solidify your expertise. Popular options include the IBM Data Science Professional Certificate or university programs like Harvard’s Data Science Certification. These certifications cover the essential data science techniques and tools that are valuable in product management, such as data wrangling, statistical analysis, and building machine learning models. Exploring our Data Science Learning Path is another alternative to refining your data science knowledge.

Building Experience in Data Projects

Practical, hands-on experience is crucial for transitioning into a DSPM role. Once you’ve gained your certifications, seek out opportunities to participate in or lead data-related projects. This will allow you to apply your knowledge and hone your skills in managing data-driven product development.

You can gain this experience by collaborating with your company’s data science team or working on independent data science projects.

By working closely with data collection, model building, and product design teams, you’ll better understand how data products are created and launched. It’s also an opportunity to observe how product managers work, gain experience in roadmap development, and refine your ability to manage product lifecycles.

What is a Data Science product? It might be a machine learning algorithm that is meant to be deployed across vast amounts of data, a fully integrated suite of dashboards meant to keep the business informed about its daily function, or any number of alternatives.

As a DSPM, not only do you have a strong understanding of machine learning and the needs of your engineers, but also have the ability to develop business cases for complex and changing technologies and communicate with stakeholders about the product in development. A DSPM is involved in the evolution of a data science product from its inception to well after its initial deployment, serving as a crucial source of input in its maintenance and continued development.

Some of the day-to-day roles and responsibilities of a data science product manager are:

• Managing a product that involves the deployment of machine learning models
• Analyzing data to influence product decisions
• Balancing trade-offs between different possible machine learning algorithms
• Communicating between a team of Data Scientists, Machine Learning Engineers, and external stakeholders
• Prioritizing the next steps in the Data Science product lifecycle
• It’s important to note that a Data Science Product Manager, who manages a Data Science product, is distinct from a Data Product Manager who manages data as a product.

While there are some similarities between the two roles, such as acting as a bridge between internal stakeholders and a data science team, a DSPM must have a deeper understanding of the actual technical process of developing a Data Science product rather than merely dealing with the outcome (the data) that such a product makes available to a client.

While a data science background can be beneficial, it’s not essential to starting a career as a data science product manager. Since it’s primarily a management role, the focus is often more on communication, decision-making, and leading cross-functional teams than on deep technical expertise. Here is how you can approach it:

Leverage Existing Skills

If you have a strong background in business analysis, strategy, or marketing, you can leverage these skills to understand the business context and how data can drive value. Even without deep data science knowledge, having a basic understanding of data concepts, such as data types, data cleaning, and basic statistics, can help you communicate effectively with data scientists and engineers. For example, understanding the difference between structured and unstructured data or knowing how missing data can impact models will enable clearer communication in cross-functional teams.

Gain Product Management Experience

Since the DSPM role is a senior position, having prior product management experience is often necessary. To get started, consider taking online courses like Google’s Product Management Certificate to build a strong foundation. Focus on developing core skills such as product strategy, roadmap planning, and user research. For hands-on experience, you could even lead a small product initiative at your current job or work on side projects where you manage the lifecycle of a product feature from ideation to launch.

Learn Data Science Basics

Understanding data science and machine learning basics could be critical for the data science product manager role you’re seeking. Enroll in our Learning Paths and solve data science interview questions to learn the fundamentals of data science, including statistics, machine learning, and data visualization.

These courses will introduce you to core tools like SQL for data querying, Python for data analysis, and data visualization platforms like Tableau or Power BI. Additionally, practicing with real-world datasets on Kaggle or completing projects that involve building predictive models can give you the confidence to communicate effectively with your team.

Network with Data Scientists

Building a robust network with data scientists and existing product managers can land you your dream job in DSPM at your favorite company. Attend data science conferences, meetups, and webinars to connect with data scientists and learn about their work. You may also participate in online forums and communities related to data science and product management.

Attend industry events like the Open Data Science Conference (ODSC) or local data science meetups to connect with professionals in the field. Additionally, online platforms like LinkedIn and Kaggle allow you to engage with data scientists, ask questions, and share insights.

A great way to build your network is to participate in open-source data science projects, where you can contribute even as a product manager, helping shape the project’s direction by prioritizing features or ensuring alignment with end-user needs.

Demonstrate Data-Driven Thinking

Even if you lack a data science background, demonstrating your data-driven thinking can give you an edge over other candidates. Analyze case studies or real-world examples to understand how data can be used to solve business problems. Moreover, take on personal projects that involve data analysis to showcase your ability to think critically and make data-driven decisions.

Highlight Soft Skills

As discussed, more than technical skills, strong communication and management skills are essential for effectively collaborating with data scientists and stakeholders in DSPM roles. Demonstrate your ability to identify problems through behavioral questions, analyze data, and propose solutions to stay on the interviewer’s radar.

Here are two case studies of individuals from different career backgrounds who transitioned to data science product management. See if you can relate to them:

Case I: The Data Scientist Turned DSPM

Sam has worked as a data scientist for five years now, and he can’t seem to get on the same page as everyone else. He’s less concerned with refining the same old model to make it one percent more accurate than making sure the algorithm is useful to the customer. He doesn’t have anything against his coworkers—he just doesn’t experience the joy of data science for data science’s sake.

So, he resolves to make a change in his career. He takes a few business classes at night and an online course on product sense. Then, he puts out his updated resume on LinkedIn and Indeed, and soon he’s interviewing for a data science product manager role. At first, he flounders in his interviews, but he begins to practice for the questions he struggles to answer the most: product sense and business case questions.

Within a year, he lands a new job as a data science product manager at a start-up specializing in providing machine learning solutions in a B2B context.

Case II: The Product Manager Turned DSPM

Alice has worked as a product manager since she graduated from college with a Master of Business Administration. She’s job-hopped from company to company and has developed a reputation for taking any product and turning it into the company’s flagship. The only problem is that Alice feels like she’s learned everything she’s going to learn and is bored in her current role.

Alice has recently found herself gravitating toward data science as someone interested in future-facing products with durative value for the customer. She wants to head a data science engineering team charged with making deep learning algorithms that help chart a course for businesses with innovative models and the customer at the forefront.

So, she resolves to make a change in her career. She begins with an online course on machine learning. Then, she goes back to school and immerses herself in the world of data science with a specialization in natural language processing.

Afterward, she puts her updated resume out to the world and finds that many companies are looking for a candidate with her new skill set. Her job-hopping experience and practicing machine learning interview questions have made Alice a pro at the interview process so she can find just the right job.

Now, Alice works as a data science product manager for a company that builds recommendation algorithms for various B2C streaming companies looking to give their consumers a totally new kind of viewing experience.

If, like Sam and Alice, you’re looking to be a data science product manager, here are some questions that you might encounter during the interviewing process. We’ll look at questions concerning product sense, SQL, and machine learning:

Product Sense: Green Dot

Let’s say we’re working on a new feature for LinkedIn chat. We want to implement a green dot to show an “active user” but given engineering constraints, we can’t AB test it before release.

How would you analyze the effectiveness of this new feature?

Watch this video for the full solution:

SQL: Employee Salaries (ETL Error)

Let’s say we have a table representing a company payroll schema.

Due to an ETL error, the employees table instead of updating the salaries every year when doing compensation adjustments did an insert instead. The head of HR still needs the current salary of each employee.

Write a query to get the current salary for each employee.

Assume no duplicate combination of first and last names. (I.E. No two John Smiths)

Here’s a hint:

The first step we need to do would be to remove duplicates and retain the current salary for each user.

Given we know there aren’t any duplicate first and last name combinations, we can remove duplicates from the employees table by running a GROUP BY on two fields, the first and last name. This allows us to then get a unique combinational value between the two fields.

Machine Learning: Job Recommendation

Let’s say that you’re working on a job recommendation engine. You have access to all user LinkedIn profiles, a list of jobs each user applied to, and answers to questions that the user filled in about their job search.

Using this information, how would you build a job recommendation feed?

Here’s a hint:

What would the job recommendation workflow look like? Can we lay out the steps the user takes in the actual recommendation of jobs that allows us to understand what a potential dataset would first look like?

Becoming a data science product manager requires a balance of technical knowledge, product management skills, and business acumen. You can successfully transition into this role by gaining certifications in product management and data science, building hands-on experience with data projects, and mastering communication and leadership skills.

With the right combination of expertise and practical experience, you’ll be well-prepared to lead data-driven products that deliver real value to users and businesses alike. Good luck!