If you’re a data analyst maneuvering through the job market, there are likely hundreds of things you’re thinking about, from how to upskill yourself constantly to how to pitch your skills to other “soft” departments. Take a second to relax and make a mental note of these six things every data analyst should remember when applying for jobs.
Analysts, in my experience, are worried about not having achieved “mastery” in certain data tools. Be comfortable with gaps in your knowledge. Remember: practice makes perfect, and you might have to fake it until you make it at first, in order to get that professional practice time in. It can be hard to keep up with a constantly evolving technical landscape and we might take a cue from Software Engineers. Internally, drill down on your core skill of being able to read and follow documentation and a capacity for trial and error. Externally, project the confidence of technical nous.
You might be a SAS specialist, a dashboard power user or the R guru, but it’s hard to beat the ever-expanding feature set of Python, and the career flexibility and paths it will offer. It’s basically at feature parity with R, with faster performance and an exciting tool set. It’ll also do wonders for your self-esteem.
Whether it’s business analysis, product analytics, or business intelligence, the lines of demarcation are not as clear IRL as they are on paper. Be an overall analyst: this is your opportunity to be a big influence on product.
While some insights can be expected, others will surprise you. That base of insights is the lynchpin that holds creative decision making together. The deeper you look, the more you’ll be able to justify your creative intuitions and bring changes to the product.
A competent data function brings a lot more than “analytically driven” decision-making. Customer segmentation can be the greatest boon on a UX, marketing automation and product personalisation.
Prioritise the quality of the data work you’ll do, and the freedom and responsibility the position will offer, even if they’re being rigorous around the Data Analyst title. It’s a common misconception to cluster all traditional analytics under an analyst title and all machine learning under data science. Get in the role, expand your operations and start implementing more “data science” techniques where the business case lies and without disrupting your existing work. You’ll end up with the skill set, the title and grateful colleagues.