The Data Science Mentor
Source: tai_blog

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 couldnt 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 wasnt 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:   Youre a mentee seeking adviceYoure an aspiring mentor eager to help othersYoure part of an organization looking to support your employeesOr you just enjoy my stories!The Dawn of the MentorThe 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:   Im 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.   Lets analyze these two figures to understand them better and generalize their needs  ultimately creating useful guidelines.   Data MenteesEven though its 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.   Ive 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.   OrganizationsMany 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.   What is the Mentoring about?I cannot count how many times Ive 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.   Defining a PlanThe 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  heres a list of things I always make sure are in place before someone steps into the interview process:   A well-crafted LinkedIn profile includes useful links to past projects and comprehensive details about their experience  including roles and key projects.A GitHub account featuring personal projects demonstrating their interest and eagerness to explore new ideas.Ensure the mentee is comfortable with the interview stagesboth technical and non-technicalso they know what to expect. This may include conducting some mock interviews.Practicing live coding with clear  well-explained comments.Be RealisticIn 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 dont know what to expect from mentoring  and its important to be both realistic and proactive. For example  when helping someone who wants to land a job as a Data Scientist  its 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. Thats far more realistic than claiming  I can guarantee youll get hired if you choose me as a mentor.   Stop the Mentoring!Whether youre a mentor or a mentee  its important not to get lost in the mentoring process. Ive 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 isnt a (real) superhero and cant help everyone. Some areas are simply beyond my expertise. When I recognize that Im not the right person  I either recommend someone else or explain that I wont be able to provide the best guidance in that area.   ConclusionsI see many new platforms connecting mentors and mentees  which shows that the demand is high and the need is real. Ive 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.