How to Maximize Your Impact as a Newly Hired Data Scientist

So, you’ve successfully navigated the daunting tech job market and secured your first role as a data scientist? Congratulations – the hardest part is over! Or is it? According to Harvard’s Alumni Career Blog, “research suggests that an employee’s first 90 days will in large part determine [their] performance, longevity, and contribution to the company.” It is vital for a company to provide a comprehensive onboarding experience to any new employee, but that is only half the battle. It is critical to own your career and bring an intentional plan to the table as you step into your new role.

As the leader of the data scientist early career programs at 84.51°, I have mentored and coached more than 75 new data scientists – many of whom are in their first full-time role outside of an internship or hands-on classroom experience. Here is some advice I share with my teams as they begin their careers as data scientists.

It Starts Before You Accept the Role

Choosing the right role for you is the first step to maximizing your impact as a new data scientist. In “Finding the Right Data Science Role: 4 Key Criteria,” 84.51°’s VP of Data Science Lyndsey Padden highlights the importance of understanding the work happening on data science teams and making sure the work aligns with your interests and development goals. Ideally, you want to shoot for a role that provides you challenging and diverse work with plenty of opportunities to help you learn and grow. Being passionate about the organization’s work, company values and goals makes your impact on your direct team and broader organization that much easier! 

Learn the Necessary Business Context

Data scientists combine statistics, machine learning, advanced analytics and programming to generate meaningful insights for a business. Having solid technical knowledge and foundational programming experience is critically important to your success as a data scientist, but not the only area that matters. An often overlooked, though key, skill set is business acumen. Learning your new organization’s underlying business, pain points, stakeholder needs (internal or external) and objectives and key results (OKRs) will give you a leg up in generating actionable insights that drive business value.

From a five-person tech startup to a large multinational company with hundreds of data scientists, there is always business context to understand before performing an analysis, building a model or making a recommendation. Personally, I find this to be one of the most exciting aspects of data science. You can take your strong technical foundations, critical thinking and problem-solving skills and apply them to any industry as long as you strive to understand the business need and end goal.

I often tell my new data scientists that you can be the most technically talented team member, write the most efficient code and build the most accurate machine learning models, but if you don’t understand why you’ve been asked to take on a project or how it fits into the broader work of the team, you will fall short on your recommendations. This could potentially lead to costly mistakes in your model or code that otherwise would have been noticed early on in development. It also makes communicating your final results to a nontechnical audience even more difficult once you get to selling your idea to the business.

Your first task on a new team is to learn, learn and then learn some more. Be a sponge and take every opportunity to sit in on meetings with your manager or teammates, even if you’re simply a passive observer. Learning the “language” of your business can be like learning a foreign language – hearing it spoken in context and immersing yourself in conversations is a great way to get up to speed on the organization’s business more quickly.

One of the most common pieces of feedback I hear from entry-level talent is their desire for more business context surrounding the work. 

Be Curious – Ask Why

As you complete your onboarding and are asked to assist with existing projects or begin your own independent responsibilities, don’t be afraid to lean in and ask why. If you are stepping into an existing project or initiative, be sure to get up to speed on the project basics. Don’t hesitate to go beyond the surface level and question what business value this project is driving, why they are prioritizing the work, what the overall goal is and the problem that the work is attempting to solve. Newcomers are often afraid to rock the boat and reluctant to ask the tough questions, but doing so will put your learning on the fast-track. 

Asking the tough questions is important to making an impact on your team, and this extends to methodologies and processes as well. Many data scientists who have been immersed in the work on a daily basis might find it hard to take a step back and consider an alternate approach to the program … that’s where you come in! The data science field is constantly changing and evolving, and new cutting-edge methodologies and techniques are being tested and trialed every day. As someone likely joining the workforce from an academic program, you may have more exposure to new methodologies and techniques than someone who has been in the workforce for 5-10 years. Push yourself to respectfully challenge and question why a particular algorithm, model or approach was chosen, and if you have new ideas, suggest them! Don’t be disappointed or discouraged if your ideas aren’t picked up right away; there could be many reasons why something is being done – ease of model interpretability, computational efficiency or cost limitations, limited resource capacity, among others. That said, still be sure to ask “why?” 

Present Solutions, Not Just Problems

A piece of feedback I received later in my career that I wish I would have known from the outset was: As you run into roadblocks, think through how you might be able to solve them before suggesting the issue to a manager or stakeholder. It can be tempting as someone new to a team or organization to take the path of least resistance when facing a challenge and ask someone else how they would solve a problem rather than taking the time to brainstorm potential solves.

This is not to say you should spend days belaboring how to solve a complex problem without help if you’re truly stuck, but coming up with some preliminary ideas and describing what you’ve already attempted when sharing your problem will build respect with your team. The people you are working with have their own responsibilities and doing some legwork to present possible solutions will go a long way toward making their lives easier and getting you the help you need.

Soft Skills are King

You may be wondering why the focus tends on nontechnical skill sets when talking about maximizing your impact as a new data scientist. This is not to downplay the importance of strong programming skills, a solid understanding of predictive modeling or cutting-edge generative artificial intelligence. However, these recommendations are from my observations of where new data scientists have the largest opportunity to grow. The most successful data scientists I see are those who upskill their soft skills, such as presenting, as well as enhance their business knowledge and who are passionately curious about new ways to approach a task. My guidance to you is not to overlook the importance of these skill sets as you continue to upskill technically.