Mentorship has proved to be beneficial for professionals and students, irrespective of the sectors they are working or interested in. However, for data science professionals, it’s more than just beneficial; it is a necessity to have in this profession.
It has been established that being the sexiest profession of the century, data scientists are in demand. Additionally, this pandemic outbreak has forced companies to enhance their data strategies, creating a massive demand for these data professionals. However, in-ground reality, it isn’t always that easy for data scientists to land on a job.
In fact, with the automation boom, companies are now looking to hire data scientists and analytics professionals with advanced skill sets and knowledge beyond a specific domain, which makes it challenging for professionals without any guidance. On the other hand, with a vast amount of resources already available online, the data science aspirants who are willing to kick start their career in this field or looking for a change usually get perplexed while devising plans to guide their data science journey. Alongside, the data science industry keeps evolving and understanding this dynamic landscape can be difficult for data scientists.
And, this is where industry experts with years of experience as mentors come into play for data scientists. Not only they can guide these data science aspirants with their career path but also help in cutting through the clutter of information that is available online to lead their journey in the right direction.
While having mentors is critical for data scientists to enhance their career, being a mentor itself can be a learning experience. However, being a mentor comes with a lot of responsibilities, and also becoming one needs them to acquire a few skill sets to be an effective mentor, such as active listening skills, empathy and flexibility among others.
However, mentoring data scientists is an entirely different ball game altogether. Apart from acquiring these necessary skills, in order to provide an effective data science mentorship, mentors need to critically assess the technical abilities of their mentees and their interest in the specific domain before providing them guidance on their career path.
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Effective mentorship can help data science aspirants in enhancing their career graph
Mentorship has also proved to be a win-win situation for both the parties involved, where students can acquire skills and valuable tips from the best brains of the industry, it also provides a big talent pool for mentors to hire data scientists for their organisations.
Data science mentees create and oversee their own career path. However, a mentor can help them achieve what they desire in their career, and for that, mentors need to communicate and listen to mentees’ aspirations and expectations that they have from their guides.
Lakshya Sivaramakrishnan, Program Lead, Women Techmakers India, who herself has been a mentor as a part of Women in Machine Learning & Data Science Hackathon stated that approachability and active listening, and understanding the requirements of the data science mentees is critical to provide a productive mentorship.
“From my experience, I think the most important thing would be listening to what your mentees want as two different mentees can have two different requirements out of a particular mentor,” said Sivaramakrishnan. “So more than me telling on the things that are already available online, a mentor can be a clear differentiator by listening to what their real challenges are.”
Alongside, to give comprehensive advice to mentees, it is also necessary for the mentors to know them profoundly. Getting to know mentees will also help in building a stronger relationship between the two, which will help mentors to provide more effective advice to their students. For this, Sivaramakrishnan believes that asking the right questions is the key. “Also, mentors can facilitate their mentees with their own experiences, network, and resources that they aren’t aware of.”
She further added, with humongous resources available online, “you as a beginner data scientist or somebody who is looking for mentorship will just get lost in the ocean of courses and resources available right now.” And that’s why data scientists need guidance in the form of a mentor who can give them a sense of direction and help them navigate in the ocean of resources that exist in this space.
Alongside, great mentors can create situations that can help their mentees to learn their desired skills. These can be a training session, classroom sessions, workshops, conferences, connecting them to a network that’s fruitful for them as well as giving them assignments and challenges to work with.
When asked, Mamta Aggarwal Rajnayak, Retail Analytics CoP Lead, Senior Manager Applied Intelligence India at Accenture stated that mentoring data scientists can be a challenging task, especially the ones that are amateur in the industry. For that, mentors need to bring their concepts to their level and should think about what new data scientists would do and what kind of questions will come to their mind. With this, mentors would be able to address their requirements from their perspective.
Mentors not only need to make amateur data scientists understand how the industry is running currently but also need to make them perceive how the industry is going to progress in future.
According to Rajnayak, at Accenture, “when we have summer interns or data science aspirants to work with, we give them small assignments and based on those assignments we challenge them using different tools like Python, Spark etc. for different projects. We make them understand these tools and how certain problems can be solved using these tools.”
Such initiatives will help them broaden their horizon and understand how to solve real-world problems. “Further, we also give them challenges with respect to different techniques — from machine learning to traditional analytics — that they may want to use to solve the problems,” added Rajnayak.
Furthermore, to mentor data scientists providing them relevant case studies, that are readily available for expert analytics professionals who are in this industry for long, can turn out to be extremely beneficial for their learning process. These industry-based case studies will help them train on a different set of data, tools and techniques. According to Rajnayak, “It is essential for budding data scientists to get their hand dirty by solving real business problems and for that we present them with a lot of case studies to work with.”
Although the technical skill sets have always been a critical component for data science to enhance in their career path, it is equally important to improve management, leadership and soft skills for them to grow up the ladder amid this competitive environment. For instance, Swati Jain, vice president of analytics at EXL, has stated while mentoring aspiring data scientists she has observed that majority of them are not looking for technical tips, instead are keener to understand the soft skills that also include how to communicate and to manage people for getting their work done.
“When I spoke to my mentees they told me that they are well aware of how to get a package and learn it, and that’s why they are looking for more softer skills like developing relationships,” said Jain.
Swati further believes that no real-world problems can be solved without people collaborating with other people. Also, “it is critical for data scientists to understand the core business scenario in which they are trying to solve the problem,” concluded Jain.
Becoming a mentor comes with a lot of benefits, not only it helps you continue expanding your knowledge by sharing it with aspiring data scientists, who are the future leaders, but will also provide you with skilled people who can be beneficial for your organisation in future. However, to offer comprehensive mentorship to data scientists, mentors would be required to include both technical and leadership skills in their mentoring session for data scientists to grow overall.
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Sejuti currently works as Associate Editor at Analytics India Magazine (AIM). Reach out at email@example.com