We all had a mentor. Sometimes, it is a parent or just someone who dropped in your life at the right time and gave you the tools to achieve something great you always wanted to achieve. I clearly remember the individuals who shaped me, and helped me to see the paths in front of me more clearly. Then, when I started working as a Data Scientist, I remember being lost at first, overwhelmed with these great problems my company wanted me to solve. I did my best, but the turning point for me was collaborating with seniors (not always from data science) who knew exactly what I was going through, helped me shape my career, and contributed to what I am today. I quickly realized that many lessons couldn’t be learned from books alone. I needed guidance from people and professionals to show me the way. Despite having many tools and technical knowledge, I often felt a lingering sense of being lost.
Over the past year and a half, I have worked as a Data Science mentor. This role is quite broad, as my experience has shown that collaboration with a mentee can take many forms, ranging from purely technical sessions to high-level career path development.
It has been a fantastic experience where I let my brain explode under the questions of my mentee, releasing knowledge I wasn’t sure would ever be useful to someone. Apparently, I was wrong, as many people seek advice, and while helping them, I learned about many new problems and challenges faced by aspiring data scientists and companies.
If you fall into any of these categories, this is definitely the article for you:
The non-deterministic nature of many problems a Data Scientist has to solve can make small challenges appear significant, which can be frustrating for companies and aspiring data scientists. It requires experience to say confidently:
I’m confident that we should proceed in this way
Regardless of how accurate your model is. Under the right circumstances, a mentor can make this process less painful and smoother.
I see two key players in the search for a mentor. The first is the potential mentee, who may be aware of their needs and ready to take action. The second is often an organization that may struggle to fully support its employees due to a possible lack of expertise within its teams.
Let’s analyze these two figures to understand them better and generalize their needs, ultimately creating useful guidelines.
Even though it’s been a while since the famous article “Data Scientist: The Sexiest Job of the 21st Century” was published, I still consider it a relatively new field, primarily due to our challenges. On one hand, best practices are still evolving and are not as well-established as those in software engineering. On the other hand, domain knowledge, which demands real-world experience, plays a crucial role. Combining these two aspects is no easy task.
I’ve enjoyed working with many individuals in this field and noticed three broad categories of people.
The first group consists of aspiring data scientists coming from completely different backgrounds. They often feel overwhelmed by the vast amount of online courses and TikTok videos claiming to teach how to (not) become a data scientist in just five steps.
The second group consists of engineers, typically from the tech industry, who are transitioning into data science. Their motivation is often rooted in hands-on experience with relevant technologies rather than simply following a trend.
Lastly, junior or intermediate data scientists actively seek guidance. This is often due to a lack of senior team members, leading to a need for direction and advice when making critical decisions.
Many of my collaborations have been directly sponsored by companies because they recognize their employees' need for support in areas that the organization cannot fully provide. This is a very honest and proactive approach to fostering continuous learning, rather than simply paying for a Udemy subscription that often goes unused.
This scenario typically involves junior data scientists who lack the support of a senior figure but are still expected to tackle complex tasks. Bringing in a “part-time senior data scientist” can make a significant difference in these cases. The ultimate goal is mentoring and developing internal professionals to the point where they feel confident proceeding independently.
My suggestion is to actively listen to employees and provide a learning service that benefits both the organization and the individual. This approach creates a win-win situation, fostering growth and development on both sides. This kind of engagement leads to one of the most effective and rewarding learning experiences possible.
I cannot count how many times I’ve been asked this question. To me, it is one of the hardest. Each request is a custom request, and each path needs to be tailored around the mentee. There are many common factors, of course, and I learned how to optimize this process, but this is exactly the reason why I cannot just make a YouTube video that works for everyone.
The first step is having a clear plan so the mentor can provide guidance and ensure the process will eventually conclude. Some people prefer a structured approach with a list of tasks and assignments, while others like to keep sessions more dynamic, adapting the collaboration based on their weekly challenges. For example, here’s a list of things I always make sure are in place before someone steps into the interview process:
In either case, whether I formalize a plan or not, I always start by asking what the goals are. This step is crucial because many people don’t know what to expect from mentoring, and it’s important to be both realistic and proactive. For example, when helping someone who wants to land a job as a Data Scientist, it’s key to clarify that while no one can guarantee a job within a set timeframe, we can focus on being well-prepared and controlling all the factors within our reach. That’s far more realistic than claiming, “I can guarantee you’ll get hired if you choose me as a mentor.”
Whether you’re a mentor or a mentee, it’s important not to get lost in the mentoring process. I’ve worked with very smart individuals who extended the mentoring without a clear reason, and while this was financially beneficial for me, I realized my job was already done. We took a break and resumed after they had applied what they had learned.On the other hand, a mentor isn’t a (real) superhero and can’t help everyone. Some areas are simply beyond my expertise. When I recognize that I’m not the right person, I either recommend someone else or explain that I won’t be able to provide the best guidance in that area.
I see many new platforms connecting mentors and mentees, which shows that the demand is high and the need is real. I’ve also noticed that data science tends to be the most in-demand topic, highlighting the high demand for talent in this field and the relatively weak supply. I believe boosting your career with a mentor under the right circumstances can be very beneficial and help bridge this gap.
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