How to Find a Data Science Mentor

Woman working with a mentor on data science


Darya Petrashka, Data Scientist, shares how to find a data science mentor and how to build upon the relationship.

Data Science is a highly developing field, and one can master it only through constant learning. But it is easier to progress with proper guidance: timely advice from a more senior fellow can save you from struggling. So, where do you find a Data Science mentor and how do you build mentorship relations? 


First of all, let’s specify the criteria for a potential mentor. Ideally, it is the same field professional of two-three years of seniority more to their mentee. Such a gap allows the mentor, on the one hand, to guide the mentee and give valuable, proven pieces of advice, and on the other hand, to stay relatively close in time to the mentee. The best source of inspiration for an aspiring Data Scientist is the person who recently got the job and has up to 1 year of experience. A senior Data Scientist could potentially teach a junior one a lot of things, but they were junior more than 5 years ago! 

The second important thing for a mentor is the ability and willingness to help, despite how simple it may sound. It includes availability (for example, suitable time slots), attentiveness, and readiness to give constructive feedback.

Last but not least is interpersonal chemistry. It is way easier to build any kind of relationship (including professional ones) if people have common ground. It would be perfect if the mentor and mentee had something in common besides work. It could be a hobby, a sense of unity and solidarity (for example, they both can be members of a group underrepresented in IT), or even a matching taste.


There are several options to consider.

Maybe, the most popular one (and the easiest) is to find a mentor at work. It is often a person from the same team (or a close one) but has several more years of experience. Happily, many companies have a mentoring process established and well-organized. In such cases, the mentor’s duties are not only to help their mentee with work tasks but to build a development roadmap, track progress and evaluate performance and growth.

But what if there is no such possibility? The second option is to look for a mentor on a dedicated platform. Some of them charge money for sessions, but there are free options as well. One such platform is the mentoring club. It is simple to use: find a person potentially interesting to you (use filters by technical area, search by query/country/etc.), view their calendar, select an available slot, and confirm it! Optionally, you can support the platform by donating.

Another option to explore is participating in special programs and initiatives focused on mentorships, such as AWS She Builds Mentoring Program or Women Who Code Mentoring. These programs provide opportunities specifically tailored for women in technology to connect with mentors who can offer guidance, support, and valuable insights. Engaging in these programs not only allows for mentorship opportunities but also facilitates networking with like-minded professionals, creating a supportive community, and fostering personal and professional growth.

However, finding a mentor is not limited to a single approach. It’s about exploring various possibilities, reaching out to industry professionals, attending networking events, and leveraging online platforms and programs to find the right mentor who aligns with your goals and aspirations.


Organizing the mentoring process involves both sides’ collaboration. Here are some key steps to consider:


Set up a regular basis for mentor-mentee meetings. This could be weekly, biweekly, or monthly, depending on the availability and preferences of both parties. Consistent meetings provide dedicated time for discussions, feedback, and progress updates.


Collaboratively establish clear and measurable goals for the mentoring relationship. These goals should align with the mentee’s personal and professional development aspirations. It is good to have a couple of global long-term goals and several smaller subgoals. Regularly revisit and revise these goals as needed to ensure they remain relevant and achievable.


A mentee is encouraged to share their progress and achievements during the mentoring sessions. It allows the mentor to provide guidance and support based on the mentee’s accomplishments and challenges.


It is important to create and keep an open and supportive communication environment where both mentor and mentee feel comfortable sharing thoughts, concerns, and feedback.


Both the mentor and mentee should actively seek and provide feedback to evaluate the effectiveness of the mentoring process. Regularly assess what is working well and identify areas for improvement. This feedback loop helps to refine and enhance the mentoring relationship over time.


A mentor is encouraged to share relevant resources, industry insights, and experiences to enhance the mentee’s knowledge and skills. This can include recommending books, articles, and online courses, or introducing the mentee to professional networks and communities. However, as mentoring is often a both-sided process, a mentee can also share their knowledge and recently gained experience with a mentor.

In conclusion, finding a Data Science mentor can greatly accelerate one’s professional growth and learning journey. It is essential to seek a mentor with relevant experience, a willingness to help, and common ground for effective mentorship. Opportunities for mentorship can be found at work, on dedicated mentoring platforms, or through participation in specialized programs. Once a mentor is identified, both parties should collaborate to organize the mentoring process effectively. Regular meetings, goal setting, open communication, and knowledge sharing are key elements that foster a successful and rewarding mentorship experience. Embracing mentorship can lead to invaluable insights, personal development, and a supportive network in the dynamic field of Data Science.

About Darya:

Darya Petrashka, data scientist

AWS Community Builder, works as a Data Scientist at SLB. She is passionate about data and its usage for problem-solving. The area of interest includes classical ML and NLP, as well as working with AWS services. An eternal student, she likes taking part in online schools, courses, and workshops. She shares insights on her Linkedin page and medium blog.




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