Maggie: My Data Analyst Career Journey

Hi Maggie! I'm super pumped to feature you on DataAnalyst.com. Please introduce yourself to our readers.

Hello! My name is Maggie, I’m based in Chicago and I’ve been working in analytics and data science for close to seven years, but before that, I worked in marketing roles for over a decade. I’m also active on social media (known as “Data Storyteller”) where I talk about my career pivot and also share advice for a career in data.  

Please share with us your current Data Scientist role, what company you work for, and what your day to day looks like.

Currently, I’m a Data Scientist focused on Product Analytics for one of the largest corporate/business travel management companies in the world. My job is to use data to create a better user experience on our product, a self-serve online platform for booking transportation and lodging for business trips.

My work typically includes experimentation (A/B testing), defining new metrics, analyzing user behavior and feature adoption, and answering product managers questions with data.

To provide more detail:

Experimentation - A/B testing is very common in product (and marketing) analytics roles. My responsibility is analyzing the results of the test - have we reached significance? Which version performed better based on our success metrics? This requires being comfortable with basic statistics, specifically hypothesis testing. I also provide guidance on how to conduct tests, which requires not just knowledge of experimentation best practices but also enough knowledge of our business to know what makes sense to test and which success metrics we should use.  

Big Projects - The analytics team usually has a set of goals/big projects that we are working on, each data scientist supports anywhere from 1-3 of these big projects throughout the year, working in groups of 2-4 often for 6 months or more. The work includes discovery (what’s the business case? what problems are we trying to solve? what data is available?), analysis (explore and visualize the data, could just be basic descriptive statistics or more advanced methods like predictive models), summarizing insights and recommendations, presenting to various audiences (analytics team, stakeholder teams, leadership), implementation (could be a dashboard or a data model or a machine learning model or just a set of recommendations), and then measuring the impact or outcome.

Ad Hoc Tasks - These are requests from stakeholder teams to help them make decisions. Sometimes the task is related to data collection, or a “quick” query and calculation of data to help them make a decision. Usually these tasks take a few hours.

One question I get asked a lot is how much time I spend in meetings, for me it’s about 25% of my time. This includes things like weekly or bi-weekly meetings with stakeholders (in my case, product managers) to understand the problems they are trying to solve and share insights from my data analysis. There are also meetings with other members of the analytics team, such as 1:1s with my boss to review my projects, status updates with the rest of the product analytics team, project-focused meetings with other data analysts/scientists and sometimes stakeholders, “all hands” meetings with the entire analytics team (there are about 30 of us) to review company or team updates or share our work with each other for feedback.

I’m also asked how often I have to give presentations. I would say big, formal presentations that last anywhere from 10 minutes to an hour for audiences of between 5-25 (but sometimes over 100) people happen about once a month or so for me. More common - probably multiple times per week - I’m informally sharing my analysis and insights with a smaller audience of 1-5 people, usually other analysts/scientists and/or product managers.  

The tools I use most frequently are SQL, Python, Adobe Analytics, and sometimes (very rarely) Tableau. (We have a separate BI team that creates our Tableau dashboards.)

Can you share with us how you entered the data analytics industry, and tell us about your first data analyst role?

I didn’t actually seek out a career in data. It more or less found me.

Originally my plan was to major in Computer Science. But my first class was very challenging and turned me off from the field. I switched my major to Communication and spent over a decade working in the marketing & public relations.

Marketing sounded fun from the outside, but once I was in the job, I realized it didn’t fit the type of things I actually enjoy doing. However, managing websites and social media was part of my role, and with that came web and social media analytics. I really enjoyed being able to dig into data, but it was a tiny part of my job. I wasn’t doing anything advanced, just reporting basic metrics using tools like Google Analytics and Excel.

Eventually, a marketing team created their first dedicated marketing analytics roles, and I was moved into one of them. I didn’t ask for it, really it came down to the job I was doing was getting shuffled to other people and they had to put me somewhere on the team and I had shown an interest in data analysis.

However, getting moved into that role was a blessing. I enjoyed working in analytics so much more than any of my prior marketing roles. Most of my work was focused on measuring the success of our B2C and B2B digital marketing - websites, social media, email, search. I was still using tools like Google Analytics and Excel but I also started to learn Power BI and R.

For the record, this happened when I was 34. So if you’re in your 20s or early 30s and haven’t yet figured out your career, don’t worry. There is still plenty of time to pivot.

What is it that you're personally finding most exciting about being a data analyst?

I am very literal and my brain is very analytical. I tend to overthink things and I’m very curious. I initially picked communication and marketing because I wanted a creative career, something that sounded interesting and exciting. But once in those types of roles, I didn’t find them very interesting. I enjoy working in a field that is logical and straightforward. You still have to be creative, but guided by data or reason. Also, I genuinely enjoy math, that was my best subject when I was younger. I feel like finding analytics brought me back to what I always enjoyed and how my brain likes to work.

How did you start improving your skillset as a data analyst? What are you currently doing to keep up with the ever changing data analytics industry?

I was lucky that I was able to land my first analytics job despite a lack of proper training. I got that role because I had a ton of business knowledge and a good reputation with the leadership on my team. When I asked why they were moving me into the analytics role instead of hiring someone experienced, they said it would be easier to get me up to speed on the technical skills than get an external candidate up to speed on all my business knowledge.

However, I quickly realized that if I wanted to move up in this field (and get a better paying job), I needed to learn a lot. I enjoyed what I was doing, so I was excited to learn more - my mindset was “I want to learn everything I can about data.” But I knew my boss wouldn’t have time to teach me everything.

So, I enrolled in a Masters of Science in Data Science program part-time while continuing to work full-time. I considered bootcamps or certificates, but I wanted to aim high and open every door possible. I was excited about this career path and wanted to get all I possibly could out of it - so I wanted to put all I possibly could into it.

Getting my Masters degree was tough. I don’t recommend graduate school to just anyone - it is not only expensive (at least, in the US), but it is a lot of time, energy, and stress. Because I was doing the program part-time, it took me four years to complete. In addition to working 40 hours per week, I was also doing anywhere from 10-40 hours per week of studying on top of that. I started feeling burned out about half way through and was extremely burned out by the time I graduated. Not to mention all of the fun stuff I had to miss because I had class or I was studying. I gave up all of my hobbies and my social life dwindled.

Despite all of that, personally, graduate school was the absolute best thing I’ve ever done for myself. It was challenging AF but I graduated with distinction - I proved that I have the chops to succeed in this field, and I can learn whatever is necessary. And I don’t have to worry that fewer doors will be open to me down the road because I didn’t aim as high as I could.

Can you share what factors are most important to you in a data-focused career and why? Do you have a vision of what you'd like your next career growth to look like?

It will always be important for me to work at a truly data-driven company. This hasn’t always been the case, and it’s frustrating when stakeholders say they want data but never actually use it. I love working with stakeholders who actually use my insights to make decisions, and who continue to want more insights from our team. It helps to feel like your work is valued!

Eventually, I’d like to move into a people manager role, although I’m not sure when, because I still enjoy doing the hands-on work. But I would like to help shape the future of the industry and also mentor and support the career growth of those who come after me. In the meantime, I’ve been getting involved in the community as an ambassador for the Women in Data Science conference, and I also help run a mentorship program through the Data Angels Slack community for women.

As you previously mentioned, you initially started your career in marketing, and then successfully transitioned into the data field. What recommendations would you give to someone who is looking to switch careers, move into the data industry and get their first full-time data analyst position - after already having experience from different industries under their belt?

Look for opportunities where you already work! So many people will reach out to me, a stranger, because they want to pivot to analytics or data science, and when I look them up on LinkedIn, I see they are already working at a company that probably collects tons of data and likely has a team of data analysts/scientists.

The internal transfer is going to be the easiest way to break in. You already (presumably) have a good reputation at the company, you already understand the business, and if there is any kind of hiring freeze, hiring internally might be the only way to fill open roles. For entry or junior roles, many hiring managers would rather train an internal candidate (with a good reputation) on the technical skills than take a risk on an external hire. About 10% of the people on my current analytics team transferred internally (from BI/data engineering, software engineering, account management, etc).

So start networking with the data analysts/scientists at your company. Ask them about their projects, what skills are important, what they look for in new hires. Let them know you’re interested in joining their team some day. You have an edge over external candidates - use it to your advantage! (Also keep in mind, some companies use other titles - Decision Support, Measurement Manager, Reporting & Insights Analyst, etc.)

In the meantime - can you get your hands on data in your current role? Or pivot to a role like that? There are lots of corporate jobs that utilize data that don’t have “data” in the title. Such as all of those years I worked in marketing.  

Anything you'd like to highlight, or add?

Don’t forget the non-technical skills. Most folks I see trying to break into this industry put ALL of their energy into learning technical skills. Those are important - but they are the bare minimum. Also, anyone can learn them. If you were able to learn SQL and Tableau in a few weeks or months - so can all of the other folks applying for the same jobs as you.  

Where I see job - and even internship - candidates actually stand out is on the “soft” skills like communication, domain knowledge, business acumen, problem solving, curiosity, and being a self-starter. I’ve seen really bright internship candidates get passed over for candidates who were also bright but successfully demonstrated that they can take initiative and solve problems without a lot of hand-holding. I’ve seen a lot of really smart job candidates get rejected because they struggled during an interview to explain themselves clearly. The reality is, it’s not uncommon to have a stakeholder who lacks quantitative skills. A big part of your job is getting that person to understand your work. So start practicing. Try to explain the things you’re learning in simple terms to your partner or roommate or parents or friends (or record yourself). When you do projects for your portfolio, create a summary that someone with no context and no math skills would understand.

Extra one, out of curiosity if you have an opinion on this - With the rise of ChatGPT / Bard, do you see AI being a risk to data analysts?

Not anytime soon, no. Like anything, these are tools that you can use to do your job. So they can actually help data analysts work more efficiently and free up your time for more interesting or advanced work. This is actually a constant struggle on our team! Our leadership doesn’t want us wasting time on simple tasks, they want us using our knowledge and skills for more advanced projects. So anything we can do to spend less time on the easy stuff is a good thing.

But until a stakeholder can directly ask for exactly what they need (which is rare), and the data that a company has is perfectly clean and straightforward (which is even rarer), no, I don’t think that our jobs are at risk.

What truly does worry me about AI is the amount of trust people put in it, and the amount of influence that algorithms are gaining in our daily lives. That’s what is truly concerning. As someone who recently finished an advanced STEM degree, it was disappointing how little time was spent on how to make ethical decisions or the ethical impact of our work. I worry it’ll only get worse before we are held to higher ethical standards.  

Feel free to include links to your socials / personal site / portfolio site, if you'd like to showcase

LinkedIn: https://www.linkedin.com/in/magwolff/

Blog: http://data-storyteller.medium.com/

Newsletter: https://datastoryteller.substack.com/

TikTok: https://tiktok.com/@data_storyteller

Instagram: https://instagram.com/data.story.teller

Twitter: https://twitter.com/data_maggie

YouTube: https://www.youtube.com/@data_storyteller