How to Showcase the Impact of Your Data Science Work

You're a Data Scientist -- or preparing to land your first job -- and communicating your work to others, especially employers, so they understand your impact is essential. These five tips will help you help others appreciate your data science.

Whether you're currently working as a data scientist or are trying to break into the field, it's essential that you learn how to communicate the impact of your skills to others — such as a potential employer.

After all, the people who hire you may not necessarily have a working understanding of data science, and their primary focus may be to create a competent team that can take care of all a company's needs.

Maybe you're already working in a data science role and want to put yourself forward as the best candidate for a better-paying position with greater responsibility. If so, the people in charge of deciding which worker to promote could have difficulty determining how your prowess fits into the business's needs.

Here are five practical things you can do to convey to others what your data science achievements mean.

If you have a working familiarity with data science and the terminology used in the sector, it can be especially difficult to separate yourself from those words and definitions and remember that other people don't have the same level of understanding about the topic that you do. As you discuss your data science work, aim to avoid language that could make others continually confused as they listen.

Next, think about the skill sets you used in your data science project that people can grasp the importance of. For example, you could talk about how you built a data science project from scratch, and how doing that required identifying a problem, cleaning the data, analyzing your information, choosing a model and more.

If you choose understandable language instead of data science buzzwords, it'll be easier for your listener to understand why your project matters and conclude that you had to apply a wide variety of skills to succeed.

It could also be useful to depend on analogies. Relate your data science work to examples that virtually everyone can understand regardless of their backgrounds. One professional needed to convince a team of people without data expertise about the worthiness of building a centralized metadata repository. She ultimately used Andy Warhol's famous soup can pop art to get on the same level as the audience.

She asked them to imagine soup as the data, the can as the database, and the label as metadata. Then, they envisioned how hard it would be if they had a pantry full of soup cans without labels. She drove her message home by relating that scenario to how the lack of a metadata repository created unnecessary confusion for end users and got the understanding she wanted.

Ask yourself if there's a similar way you could apply analogies to your data science work to emphasize why it matters. This approach could be especially valuable if you're struggling to get the nod of approval from a superior about a planned data science project that requires additional funding or time.

In an ideal situation, you'd have data science projects in your portfolio that apply to every company that may give you an interview. Since that's not likely, you'll need to do the next best thing by looking at a project differently — through the lens of the company's needs. Look at the projects you've done so far, and then assess how your expertise fits into a company's current requirements or upcoming plans.

For example, if one of the data science projects in your portfolio involved machine learning or neural networks, you might focus on its efficiency or value-based metrics. This will be helpful when explaining your belief that the combination of your expertise and automation's capabilities could help a company reach the next level.

Instead of showing off your complete data science portfolio when meeting with a potential employer, choose one or two projects that best align with the issues the company is dealing with. By doing this, you can prove that you've done your research about the company and have had in-depth thoughts about how data science could benefit the enterprise.

If you've participated in any data science contests and emerged as one of the winners, be sure to mention that achievement, as well as relevant specifics about it that make the results even more notable.

For example, if you discuss how thousands of other people from all over the world took part and you ended up as a top finisher, that implies that the panel of judges saw that your project or idea had merit.

Then, it should be easier for the employer to conclude that your work had outstanding factors that others recognized in a challenging setting and that you could bring some of that talent to the company.

When you're either trying to secure a data science job with a new employer or have found yourself in the running for a promotion, you've probably dug out your resume and made sure it's up to date, so decision-makers have the most relevant information about you.

You can also showcase the impact of data science projects while writing a better resume, especially if you already have experience with putting them to work in real life. While fleshing out the section that outlines your responsibilities, call attention to specific ways the projects you assisted with brought concrete advantages to the company.

As employers review data science portfolios, they look for evidence that the work performed resulted in things like direct value to the company or its customers or generated helpful insights from raw data. You could summarize your role in making those things happen by indicating something like, "built a script that aggregated data from multiple databases and improved user productivity during data analysis by an average of 40%."

Moreover, encourage the people reading your resume to refer to the projects you specify by finding them in the portfolio you provide. That way, they can get a snapshot of the eventual payoff by reading the entry in your resume and then dive into the details.

As you put these five tips into action, always remember to illustrate the milestones of your work in ways that make sense to your audience. That method undoubtedly requires tweaking your message for each company, individual or team that hears it, and that's OK.

That extra work will reap positive results as you discover that it's possible to highlight your worthiness even to people without a strong understanding of data science.

Source: Kdnuggets.com

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