How to Become a Data Science Product Manager Without Experience
Data science product managers are in heavy demand today more than they have ever been. Thanks to new and emerging technologies, huge amounts of data are being processed, analyzed, and utilized every day. Now, businesses rely on data science professionals to interpret, understand, and manage that data.
Much like an AI product manager, a data science product manager is the person in charge of overseeing and managing data science-related projects and products.
There are a lot of duties involved in this position. A data science product manager will need to help with every stage of product development, from the initial planning phase all the way through to launch and beyond. They have to be experts in their field, possessing a range of both technical and social skills.
While data science product managers are in demand, those who wish to pursue such a career find themselves in a precarious position. There is no single path to obtaining this kind of role, and today's product management professionals can come from various backgrounds.
That said, there are some common steps that you can follow to land this type of job.
This guide will look at some of those steps, along with pieces of advice to keep in mind if you hope to succeed as a data science PM.
Step 1: Get Certified In Technical PM
Certification is an important part of the process of becoming a data science product manager. The best way to start your training is with a course that covers the fundamentals.
Being a product manager involves juggling several responsibilities. You'll need to have a solid understanding of both the technical side of the process, as well as the social, organizational, and managerial aspects of the job.
On the technical side of things, companies expect that product managers have a deep level of data science knowledge. You'll need to be aware and up-to-date in terms of the latest breakthroughs in the fields of data science and machine learning. This will aid you in terms of being able to manage the design, development, and launch of DS-related products.
Having plenty of technical knowledge on the subject will also aid you in terms of understanding why the product is being made, how it can help people or businesses, and what needs to be done during its development. This can help when it comes to communicating with your technical team of data scientists.
Plus, product managers who are well-versed in the technical aspects of product management should also find it easier to oversee the product roadmap.
Take a look at our courses on product management certification.
It can also help in terms of teaching you the fundamentals you will need to succeed in the role of data science product management.
Step 2: Learn Fundamentals of Product Management
Data PMs have a lot of responsibilities to cover on a day-to-day basis. These include:
Being able to analyze market trends
Predicting customer needs
Anticipating strategies and tactics used by competitors to help your product remain valuable in its respective market
Knowing how to manage multiple teams
That's why you cannot rely on your data literacy alone. Even the finest data scientists cannot transition into product management without proper training and preparation beforehand.
Even if you're a pro in the technical side of things and have a vast knowledge of machine learning models and computer science, you'll still need social, management, leadership, and organizational skills to succeed.
Step 3: Get Certified In Data Science
Identifying business opportunities, assessing performance metrics, and leading technical teams are key when managing any kind of tech project. However, those kinds of skills alone are not enough to become a respected product manager. You'll also need to have an expert-level understanding of the principles and purposes of data science.
Data science is a broad and complex field. There are many aspects to it and many ways in which businesses across several industries can utilize it. Many people dedicate their entire careers to studying data science and finding ways to use it for the betterment of businesses and mankind. You don't need to go that far, but you do need to know a lot about data.
Any company hiring product managers will be looking for candidates with a deep understanding of data science. They'll want to find someone who knows the methods, processes, and algorithms involved in assessing data and extracting valuable insights from it. They'll also be looking for someone who can see real-world applications of data science to help them build a successful product.
The next step of becoming a data science PM is therefore to make sure you are certified in data science. This may involve obtaining a degree of some kind in the field of data science, which is how many professionals start off. There are also plenty of courses that you can take online. This is often a smart option for people who already have careers and need to study while balancing their work lives.
Step 4: Gain Formal/Informal Experience in Data Projects
Having the knowledge you need to succeed in product management is a huge part of the process. If you can get the certifications and qualifications listed above, you'll already be on the right track to achieving your aims. That said, having working knowledge is one thing, but putting that knowledge into practice and getting some hands-on experience is different.
Once you're certified and up to date with the latest data science trends, it's time to put the things you've learned into action. You'll need to start getting involved with data science projects and product developments in order to get a feel for what is involved in this process. There are a few different ways in which you can do this:
- One method is to join up with existing product development teams that work on data sets and data-related projects. You don't have to step into the manager's role right away. Instead, you can begin by working as part of the data collection team or as part of one of the other teams involved in the process.
- From there, you can observe how data science projects tend to flow as well as how the PM operates. Not only will this help you see what a key role the PM has, but it will also help you monitor how they solve problems, work better with different teams, and manage the product lifecycle, among other things. In addition, you'll also be picking up valuable experience to bolster your resume.
If you can’t work with data science product teams right away, then work on your own projects. There are plenty of examples of budding data science PMs who create their own data analysis products and projects in their spare time. You don't even have to see the project through to the end. Instead, you can create a possible product roadmap and consider ways in which the product could be managed and developed.
How to Become a Data Science Product Manager With no Experience
Many aspiring product managers worry about how they can break into the field of management without having any prior experience. After all, many tech positions in today's world demand a certain level of experience, and it can seem impossible to obtain certain roles without years of experience.
However, statistics show that a lot of PMs enter this position with little-to-no experience in product management. In fact, a lot of them start off in other areas of the data science product development cycle. These include data analysis and data collection. They then transition into management later on after picking up the necessary skills needed to take on the job.
There are also data product managers who started off in other kinds of product management. They might have had a role as an AI product manager, for example, or even have experience in product management in a different industry such as the automotive industry. They then transition into the world of data sets and bring their management, organization, and communication skills to succeed.
Therefore, it is possible to break into the world of product managers without having done so before. In fact, many existing product managers recommend that those who are interested in product management in the future begin their careers in other areas of data science and build up to PM over time. This allows them to develop their technical knowledge and sharpen their managerial skills.
A good thing about getting into product management is that you can pick up many of the key skills in other lines of work. Here are some of the things you should try to work on:
You'll also need to have the necessary knowledge of data science. Therefore, you should try to choose a career path or qualifications that provide you with this expertise.
Prepare Yourself For Your Next Data Science Product Manager Role
Taking on a data science product manager's role can be a rewarding and enjoyable step in your career. But if you hope to succeed, you'll need to prepare yourself. Here are some key steps to keep in mind to get you on the right track.
1. Keep Up To Date with Data Science Developments
If you hope to succeed in overseeing data science projects, you'll need to have an expert-level understanding of data science. You must also be aware of the latest breakthroughs in this field. Remember that data science is always evolving and changing as time goes by. Here are some areas to try and focus on:
- Data science fundamentals
- How data science can be used across different industries
- Deep learning and machine learning projects
- The latest data science new solutions and why they were made
If you're just beginning in this field, it's a good idea to take educational courses and learn as much as possible.
If you have more experience, try to keep your knowledge sharp and up-to-date. Find online resources like specialist data science sites to stay informed on recent real-world developments as they happen.
2. Develop Your Soft Skills
You'll need a strong set of soft skills to handle data science projects. This is due to the fact that a lot of your day-to-day duties will involve talking to people, managing teams, organizing things, and so on. In other words, you won't only be dealing with the technical side of data science. You must have very solid soft skills to back you up. Here are some things to work on:
- Strategic planning
Try to find ways to improve these skills, both in your career and even outside of it.
In the professional sphere, you can take courses and get involved with group projects and leadership positions.
Outside of work, you can get into hobbies or join clubs that involve working with teams to make you an even more effective communicator.
3. Develop Emotional Intelligence
"Emotional intelligence" is a phrase that is used in PM circles. It also happens to be one of the most important skills for you to master.
In simple terms, emotional intelligence is similar to empathy. This type of feeling is the ability to understand other people's feelings and manage emotions. This includes emotions that you feel as well as those of others.
This is important for managers as it allows them to deal with difficult situations during a product lifecycle. This includes when things might go wrong, when your team feels under pressure, and even when your team needs some inspiration to keep pushing forward with their objectives.
Developing this skill has to do with practice and experience. Communicate as much as possible with stakeholders, colleagues, and your own product team.
When faced with problems and stressful situations, try to think about the right ways to react rather than letting your emotions decide for you.