What is a data science product manager? This may seem like an easy question to answer, but the truth is that there are many different responsibilities and skills involved in the data science PM role.
A data scientist needs to be able to have expertise in statistics and machine learning algorithms while also being able to communicate with business stakeholders. Also, they need technical knowledge as well as great communication skills.
In this post, we will explore what a data science product manager does on a day-to-day basis and why they're so important for any company looking to succeed in today's tech landscape.
Table of Contents
What is a Data Science Product Manager?
A data science product manager is a person who manages the process of turning raw data into an actionable, understandable, and usable piece that can be used to improve products or services.
The data scientist must have expertise in statistics as well as machine learning so they are able to communicate with business stakeholders. They also need technical knowledge and great communication skills.
Data Science Product Manager Roles and Responsibilities
There are many roles and responsibilities that a data science product manager fulfills.
Great product managers work closely with analytics team members to collect, analyze, visualize data points from different sources such as web traffic or customer feedback. They also have the responsibility for communicating findings in an understandable way to business stakeholders so they can leverage this information when making decisions about products or services.
The other major role is mapping out the best methods for analyzing large amounts of data sets using tools like Apache Spark along with working on predictive models which will help create better predictions than what was previously possible in traditional statistical analysis. Their goal here is similar: make things more valuable and actionable by interpreting data through quantitative reasoning skills while building relationships within organizations.
Let's take a look at some of the most relevant tasks for a data science product manager:
1. Leverage market knowledge data to amplify product development
In order to make data-driven decisions, a product manager needs to be knowledgeable about their market and industry. Data scientists can provide the necessary knowledge by examining existing data sets - such as user demographics on an e-commerce website like Amazon or reviews of customer satisfaction of other types of businesses - and using that information to shape product development strategies accordingly.
Data science products have become so sophisticated that they are now able to glean insights from raw data without any human intervention at all. It's also easier than ever for non-technical people (like marketers) to get access to these tools thanks to open source solutions like Apache Spark. This tool is now widely used in many organizations because it has been optimized specifically for the requirements of modern business intelligence tasks.
2. Apply data science techniques and data engineering processes
One of the most prevalent tasks that a data science product manager will undertake is converting raw data into something more easily digestible and usable by others in an organization - such as generating reports, analysis or summaries.
Data scientists must also be adept at taking complex problems and breaking them down to their simplest components. They're like a translator between business stakeholders who are not skilled with using numbers and those on the technical side who can understand complicated analytical models but need guidance on how best to apply these models in practice.
This process is often referred to as 'data engineering' because it typically involves combining algorithms, mathematics, statistics, logic programming languages (such as Python), and implementation details from disciplines ranging from machine learning to data warehousing.
3. Do market experimentation tests
One of the most important roles of a data science product manager is to experiment with new products and features. A good way to do that is by testing the market in an experimental environment, where users are introduced to new versions of your product and given free choice over which version they prefer.
For example, let's say you have developed a chatbot for customer service representatives; one outcome might be if it turns out customers like using voice commands better than text messages, as opposed to vice versa. You would then need to update the design accordingly before rolling out this change on your production site.
4. Develop data pipelines and warehousing strategies
Data pipelines are invaluable for extracting data from different sources, managing and cleaning it up to a certain standard, and then storing the cleansed data in an easier-to-access location.
Data science product managers usually start by identifying your most important product management metrics (such as revenue, customer acquisition cost, or conversion rates) - these will form the basis of their warehouse architecture design.
5. Learn techniques to evaluate data from live products
Data science PMs have to spend a lot of time evaluating data from live products. This means that they should know how to collect data, measure performance, and identify key metrics for their business.
6. Design and execute A/B and multivariate tests
As data science product managers spend a lot of time designing and executing tests, it's important that they have an understanding of how to design experiments. These are often A/B or multivariate tests, so the PM should know when it is appropriate to use each type based on their PM goals.
They also need to be aware of any potential biases in user behavior that could affect results - especially given that automated testing techniques can rely heavily on machine learning models for predictions about users' likely choices during trials.
7. Evaluate the output captured in statistical analyses
Data science product managers should be able to evaluate the output captured in statistical analyses. This includes understanding how different results are displayed, interpreting charts and graphs, being aware of what data is available for analysis (and where it can be found), and evaluating biases that may exist with the data set they're analyzing.
8. Bridging data science interaction with business stakeholders
Data science product managers need to be able to bridge data science interaction with business stakeholders. This includes presenting data in a way that is understandable by the people who would use it and being comfortable working with the technical aspects of data analysis as well as understanding how different tools can assist them - for example, if they're using RStudio or Excel.
9. Designing product roadmaps
This is an important and often underestimated aspect of data science product management.
Product managers need to design the roadmap and establish a suitable workflow for their products, which includes "the plan or diagram showing what should be done in order to achieve an objective". The development team needs a clear sense of where they are going with each project so that there is no confusion as projects move forward.
10. Do big data collection and analysis
Big data refers to the use of more data than what a traditional database system can store or process. The product manager needs to not only have knowledge about how big data is collected and analyzed but also understand its importance in their day-to-day activities as well as future projects that they may take on.
Doing this will require the product manager to have knowledge about data management and big data tools used by their company.
11. Work on product lifecycle and product vision
The product lifecycle is how a company deals with the different phases of development for their products. The data science product manager needs to be involved in these different stages as well as understand what they are and why it's important that they work together on them.
Product vision refers to knowing where the team wants to take their project or idea, which often includes analyzing competition and other factors outside of just collecting data from users through surveys. Data scientists need this knowledge because understanding all aspects of a potential future project will help them identify ways that can make it successful when dealing with market research and user feedback later down the road.
12. Work with the Scrum team
The Scrum and data science teams are composed of a product manager, data scientist(s), and the engineering team. The data science product manager will work with Scrum to break down tasks in order for engineering to begin working on them while also addressing any concerns or questions that their teammates may have about how they go about doing so.
13. Dealing With Data Science Complexity
As data science becomes more intricate and complex, data science product managers have to work harder at making sure they understand what is going on with the machine learning models that are being used.
14. Presenting Data Science Research
Product Managers present their findings in a way that's digestible for everyone- from the product team to executives. This often means explaining technical concepts in layman terms while also showing tangible results and outcomes of an experiment or analysis through graphs, charts, statistics, etc.
Data Science Product Manager Skills
There are some product management education requirements in order to become a data science product manager. You can enroll in a product management bootcamp or take a product management certification course. Doing this will allow you to gain important skills that you will use every day.
In order to be a data science product manager, it's necessary to have the following product strategist skills:
1. Understand the lifecycle and development of data products
This is a crucial skill for data science product managers because they have to know how products are developed in order to be able to assess where the bottlenecks might be and what type of help a company needs.
Additionally, understanding the lifecycle can give them an idea of when certain tasks should happen (e.g., design thinking or prototyping).
2. Knowledge in statistics, SQL, and machine learning
Being a data science product manager, it's important to have knowledge in at least one of these areas because they are fundamental tools that data scientists use.
In order for them to be familiar with the different types of datasets and how they can collect from web-browsing or other sources, this is just as necessary.
Knowledge in statistics will allow them to interpret numbers and understand what those numbers mean. Knowledge in SQL will help them work efficiently when accessing databases on big company servers since many companies store their data there.
Lastly, machine learning skills make up an essential part of any data scientist's toolset - especially if you're trying to build products based on predictive models (e.g., fraud detection) or personalization.
3. Solid data analytics knowledge
This is very important as data science product managers need to be able to find patterns in the data and make correlations.
They also have to know when they are seeing a trend that needs more investigation or is just random noise. Data scientists must understand their own biases (e.g., what do I want my data analysis to show) and question whether it's an actual pattern, correlation, or bias - or not.
This means thinking outside of the box about possible solutions for business problems, as well as understanding which solution would work best with available resources under time constraints.
4. Communication skills
Communication is an essential skill for data science product managers. It's important to be able to communicate both the findings of data analysis as well as what can realistically be accomplished with available resources in terms of implementing solutions or changes.
Data scientists must also have excellent listening skills, so they can understand and relay back data product management stakeholders' needs without losing information along the way.
Taking care that all team members are heard is an essential part of communication; this means empathizing with their feelings and understanding where these people are coming from when it comes to business objectives but also making sure that disagreements or misunderstandings don't cause major conflicts within the company structure.
6. Knowledge in data storytelling
In order to successfully data science research, a data scientist needs to be able to communicate their findings.
Data scientists need the skillset and mindset that can conceptualize solutions for an organization's problems in the form of data-driven stories.
That means being knowledgeable about what kinds of narratives are most effective and who they're aimed at, just as much as knowing how best to approach stakeholders with ideas or get buy-in on proposals.
Knowledgeable storytellers create more persuasive content; which is exactly what you want when trying to convince others behind your idea - especially if it involves change.
Frequently Asked Questions:
What is the Average Data Science Product Manager Salary?
According to Zippia, a data science product manager can earn an average salary ranging from $89,000 to $159,000, being $119,293 the average salary in the U.S.
Why are Data Science Product Managers Important in a Company?
Data science product managers are important because they are the person many data scientists report to.
They can also be responsible for architecting and developing a data science project from start to finish in order to bring it into production, which is crucial when it comes to ensuring that clients receive maximum value from their service or product.
With this in mind, the data science product manager career path can be one of the most rewarding roles in data science.