As an aspiring data analyst, you want to make sure that your resume is top-notch and catches the attention of hiring managers. It’s only real purpose is to get you through the screening process and into an interview. To help you get the most out of your resume and help stand out from the rest, we’ve compiled a list of keywords that you should consider including in your resume. In some cases you might find it difficult to include all of them or that you can convey the same meaning without using the actual word and that’s OK. The main thing is that you show employers the types of behaviors these keywords convey. By choosing the right keywords and putting them into the right section of your resume, you’ll be increasing the chance that your resume will be read and, eventually, lead to an interview! Happy job hunting!
Actionable
There is no doubt whatsoever that data analytics is playing a significant role in business today. The ability to quickly and easily derive insights from large data sets has helped businesses of all sizes identify and capitalize on opportunities that may have been overlooked or taken by competitors. However, in order for analytics to be impactful they must be actionable. When creating your resume ensure that you focus on how you, or someone you were helping, was able to take action from your insights. Analytics without action is just academics.
Users
You may be familiar with the term “users” in relation to IT. However, users can also refer to individuals or groups who rely on data analytics for their work. In order to catch the attention of hiring managers and show that you are knowledgeable about how data is used by real people, make sure to identify at least one user-focused project that you worked on during your career journey. If you don’t have any analytics projects that you’ve done for users then be sure to use an example that demonstrates some sort of customer experience. Even if that experience was in a restaurant or in retail, helping users/customers is universal.
Strategic
The need for data analysts to be strategic is critical in order to ensure the success of any organization. Data analysts are responsible for understanding and interpreting large amounts of data so that their users can make informed decisions that impact the organization. For that reason it is not enough to just be strong in SQL or Python, you must show that you are an applicant that can understand the broader strategy of the organization and use analytics to support that strategy. You must understand both what your user and your organization are trying to accomplish so that you use the right analytics at the right time. If you are having trouble finding a good experience to put on your resume try adding some verbiage to your summary section or skills section.
Ambiguous
The best analysts, regardless of industry or subject matter expertise, are those that can get results despite having an unclear path to getting them. This could be in the form of fuzzy user requirements, poorly defined processes and standards, or even a series of decisions with no clear framework to make them. Show that you can be a thought leader and forge a path on your own. Managers (at least the good ones) don’t want to micromanage, they want a team that they can trust to go out and deal with problems without always having to bring them back to leadership. Be the analyst that thrives in ambiguity.
Partnership
No data analyst resume would be complete without the mention of some sort of partnership. While similar to collaboration, forming a partnership is different for a couple of reasons. First, collaboration often implies you are both working on delivering the same thing and both need to contribute to reach your goal. Partnerships on the other hand may involve some collaboration but often times the two parties have completely separate roles and responsibilities and therefore give and take at different times to make sure both sides succeed. Second, because the two parties have different roles and responsibilities a partnership must be formed over time. It requires building trust and being unselfish. Provide a resume example where you formed a strong partnership that served both sides well and you’ll be a step ahead.
Scalable
Not so long ago the goal of analytics in many companies was to empower leadership so that they could make better decisions backed with data. Today the stakes have been raised and entire organizations want to be data driven. This means that you are no longer just supporting leadership but everyone in the company. For that reason scalability is a hot topic in many analytic organizations. Provide examples of how you’ve gone from helping 10 people to 100 people, how you’ve found a way to put data into more hands, and how you think of all of your users. Show that you are focused on scalable solutions that can serve the many and not just the few.
Translation
A key component of data analysis is the translation from business problem into analytics and then again from analytic results into business terms. For example when you take a real world scenario you likely create some sort of data model in SQL or Excel to represent the the state of the world and then build some sort of statistical model to measure how one variable affects another. In that case you are translating the scenario into data & analytics speak. After your analysis you may be left with a coefficient, a Tableau visualization, or other numerical data that the average person can’t interpret. You then translate the results back into your real world wording so that a non-technical person could understand it.
Results
One of the most common mistakes I see on data analyst resumes is talking about analytic activities (I created a report/dashboard/model) but not finishing the example with what business results those activities provided. As much as possible in your experience section try to talk about your analytic activity, what action it allowed someone to take, and what business result it provided. As much as possible use numbers to quantify the results. Not only will it help show that your are providing real value but it will demonstrate that you are thinking about the business and not just your own little world.
Implement
Implement might sound like an IT keyword but its place in analytics is becoming more and more important. In my mind the difference between creating a model or a report and implementing one is ensuring everything is in place for it to be useful over time. You have documentation, you have things in place to ensure quality and accuracy, you have a process to make updates as needed. Putting in these things, while not always the most exciting, ensures that your work stays an asset and doesn’t turn into a liability. Show that when you build something you built it to last.
Conclusion
Hopefully, you have found this blog post helpful. If you have any questions or want to talk about how to get started in the industry set up a Career Breakthrough Consultation with me. It doesn’t cost a thing, there is no selling during the Zoom call, and I promise you’ll walk away with a tip or two to help you with your job search.