Data science is quickly becoming one of the most important careers. That’s mainly due to the sheer amount of data being processed and created each day. That’s why today we a data science product manager to make sense of all that data.
Data scientists tend to work with massive datasets, algorithms, machine learning models, and even artificial intelligence. They use these tools and methods to extrapolate trends, insights, and other important information from raw data.
However, despite the data being refined, you still need someone to manage it all. And that’s where the data science product manager comes in.
In this article, we’ll go over what the data science product manager does, including their roles, responsibilities, skills, and how you can become one.
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What is the Data Science Product Manager Role?
The data science product manager is someone who has to work with the data science teams, product teams, data engineering teams, and product development teams. While their roles may change depending on the industry and organization, they still have to maintain certain job roles.
The following summarizes what their roles are about.
- Understanding Customer Needs – Customer needs can often be misunderstood, leading to ill-defined requirements being sent to all teams. That results in the creation of products and features that no customer wants.
- Identifying Good Use Cases – It’s hard for a business to work out the best use cases for artificial intelligence and machine learning. Data science PMs understand business needs, giving them a unique advantage for identifying business cases.
- Figuring Out ML Solutions – Customers, upper management, and other stakeholders can’t accurately determine when machine learning is needed to solve a problem.
- Unique Skillset and Time Requirements – Managing a product requires a lot of time and skills. Data science PMs have to utilize their skillsets and minimize the time required for effective management while maximizing productivity.
- Understanding Business Needs – Data scientists alone can’t fully understand the business needs and product nuances. However, a data science PM can do both.
- Using Results for Insights – It’s hard for the customer and other teams to use metrics and results for analysis and trends. Data science PMs can understand the datasets, analyze them, and derive insights to make data-backed and informed decisions.
- Launching New Products on Time – Badly timed launches can cause a lot of problems, whether it’s a new product or a new product feature. Data science PMs can effectively manage product research, development, and management, thus ensuring everything happens on time.
Based on these roles, a data science product manager can have varying duties and responsibilities.
Duties and Tasks
The responsibilities, duties, and tasks of a data science product manager are subject to change depending on the industry, organization, and various other factors. Furthermore, it also depends on how data-centric the company is.
In any case, there a few duties, tasks, and responsibilities that all data science product managers have, including the following.
- Use a technical and data-influenced approach to find solutions to complex problems.
- Help drive product development from conception to launch.
- Work with the product owner, product team, and other stakeholders to figure out the workflow to create an MVP.
- Try to get data collection parameters in place to ensure that there are large datasets to work with.
- Help the product teams in setting up the product roadmap.
- Work with the customer teams to amplify the user experience.
- Develop API integration systems to work with various startups and SaaS companies.
- Support cross-functional teams by helping them achieve their goals through goal definition and project definition.
- Ensure that product designers have all the data they need.
- Work with the product deployment team to help customers realize product value.
The duties and responsibilities listed above are true for every data science product manager. However, there will be some additional tasks based on your organization, industry, and project.
If you’re interested in excelling as a Data Science Product Manager, then check out our certifications to help you do just that below.
Skills and Abilities
The skills, abilities, and qualifications of a data science product manager remain the same for the most part. However, their career paths can have a significant impact on their overall abilities.
For example, a data scientist becoming a product manager is bound to have more skills and abilities. Alternatively, a product manager looking to become a data science product manager will have to learn various technical skills.
In both cases, there are a few skills, qualifications, and abilities that all data science product managers are expected to have, including the following.
- Complete understanding of data science principles.
- Skills in various programming and database languages like Python and SQL.
- Understanding of machine learning, deep learning, artificial intelligence, and other concepts.
- Knowledge of product management and project management.
- Understanding of quantitative concepts like statistical models and more.
- Knowledge of big data concepts.
- Good command of various tools and software like Tableau, Microsoft Office Suite, and more.
- Bachelor’s degree in either data science, data analytics, product management, or any related field.
- At least five years of experience in product management or data science.
- Excellent research and analytical skills.
- Great interpersonal and communication skills.
- Problem-solving skills are crucial.
- Leadership and management skills are essential for managing multiple products.
The skills, abilities, and qualifications listed above are what all data science product managers are expected to have. However, some companies may require additional skills.
How to Become a Data Science Product Manager
Becoming a data science product manager can take years of experience and study. That’s because you need to understand how data science works, have crucial technical skills, and also know about the fundamentals of product management.
It’s much harder to learn the necessary skills to become a data scientist than it is to learn product management. That’s why most people’s career path takes them from a data scientist position to a data scientist PM position.
That doesn’t mean there aren’t product managers who have learned data science. There are a lot of data science and data analytics boot camps where PMs can quickly learn the relevant skills they need.
In any case, it can take years to become a master of one field, let alone both of them. That’s why data science product managers are paid well above the national average.
According to Glassdoor, the average data science product manager salary in the US is $. The typical salary range is between $ and $, with the higher end being offered in cities like New York, San Francisco, and Houston. Meanwhile, companies like Microsoft, LinkedIn, and Amazon tend to pay the most to their data science PMs.
A product manager needs a technical background in all cases. That’s why a data science product manager makes sense.
Depending on your career path, you can easily get on your way to becoming a data science PM. However, if you’re completely new, you should start by learning data science. Join a data science boot camp, start doing courses, and get certified. Practice your skills on personal projects to get started.
After that, start learning about product management through courses and books and get a PM certification too. Then, apply to become a data science product manager.
Becoming a Great Data Science Product Manager
While becoming a data science product manager can take years, it can take even longer to nail it down. However, you don’t need decades of experience to become a great data science product manager.
For the most part, you have to decide on your career path early on. That will help you get the right job in the right industry. Furthermore, you’ll have ample time to study additional subjects and learn new skills. Meanwhile, you can complete courses and certifications in both product management and data science.
Using that knowledge and expertise, you can work on personal projects to build experience.
Doing that consistently will help you get on the path to becoming a great data science product manager.
Frequently Asked Questions (FAQs)
1. Can a data scientist become a product manager?
There are no outstanding restrictions on who can become a product manager, as long as you have the right skill set and experience. A data scientist tends to understand how to manage large datasets, how to analyze them, and how to use various techniques, methods, and tools to make sense of the data. Using that experience, data scientists can easily settle into the role of a product manager.
More importantly, you don’t need an additional degree or an MBA to transition into a product manager role. All you need to do is learn about product management, agile methodologies, SCRUM, and other similar concepts while developing and growing new skills.
You can start by reading some product management books to get the appropriate knowledge. As a data scientist, you probably managed a few projects of your own, so you technically already have some sort of data product management experience. After learning the nuisances of PM, you can start to play an active product manager role to transition into that job title completely.
2. What is a data science product manager?
A data science product manager is someone who understands data science, data analytics, and data engineering, while also understanding the fundamentals of product management. While in most cases, both job titles entail different job descriptions, a lot of companies have started to merge the role in select cases.
Both data scientists and product managers have to make timely decisions and use existing metrics that help measure the outcome of any decision. When you combine both roles, one single person can make unified decisions that are good for the data science and product management side of things.
That’s because a product manager understands what success means for a product or any of the product features. A data scientist evaluates it all using metrics that help define the outcome. And if you’re a data science product manager, you can take care of and manage both aspects simultaneously.
3. What does a data product manager do?
A data product manager has to collectively manage and oversee all data-related matters for relevant products. They have to work on the product strategy, governance, and implementation of any data-related elements. Furthermore, they have to actively work with data engineers, data analysts, upper management, product teams, and customers to ensure the right data comes in.
After choosing what data is relevant, data PMs relay that data for data analysis. The data scientists study all the data, notice patterns, and trends, and share their findings with the data product manager. Using those findings and insights, they have to make informed decisions.
4. Is product management related to data science?
Product management is related to data science because the latter is used to determine new trends and insights for products. Therefore, there is an indirect and direct connection between data science and product management. Both fields complement each other because product managers manage and oversee product-related matters while data scientists choose metrics to record the outcomes of the decisions taken by the PM.
As a result, both the data scientist and PM roles play a unique role in ensuring that a product is successful. For the most part, a data scientist can become a product manager. However, if a product manager wants to also become a data scientist, they need to learn data analytics, data engineering, machine learning algorithms, deep learning, artificial intelligence, Python, SQL, data visualization techniques, and software engineering to an extent.
Therefore, it’s easier for a data scientist to become a product manager compared to the other way around.
5. Which is better data scientist or product manager?
There is no really better or worse role when it comes to data scientists and product managers. That’s because both play a key role in ensuring that a product is successful. Data scientists need to have more technical skills, such as programming skills, analytical skills, and are driven by precision. Alternatively, product managers need soft skills, along with robust knowledge of product management principles.
Therefore, both job roles play a crucial part in the product lifecycle. However, if we compare them based on their salaries, data scientists tend to earn more due to the technical skills required.
For comparison, the average data scientist salary in the US is $116,054, with the salary range between $82,000 and $165,000. Meanwhile, the average product manager salary in the US is $112,040, with the salary range between $73,000 and $173,000.