Categories
analytics bi businessintelligence everythinganalytics

Landing Your First Analytics Job

Entry Level Position – requires 5 years experience

-Every analytics job posting

This is Part 2 of the Everything Analytics series. Find Part 1 here.

Too few applicants with experience

As I mentioned in The Wandering Path to an Analytics Career, there is a ‘Great Filter’ in Analytics. It looks something like this:

Lots of people want to break into an analytics or data science career, yet not many are able to. This leaves a glut of competition for entry level positions, and not enough qualified applicants for mid-level to senior positions. Once you get your first few years of experience, you’re golden! You have your pick of many options within the data world – but you have to get past the Great Filter.

This rings true for me in an anecdotal sense – I have experienced this as a job seeker, interviewer and in discussions with data hopefuls. Given this is a blog devoted to data, I wanted to quantify the interest in a analytics positions just posted on LinkedIn. Unsurprisingly, you’re swimming upstream if you’re just blanket applying to analyst jobs – dozens to hundreds of applicants within a day or two of posting. See below:

For a fantastic & further in-depth analysis, I highly recommend reading the “Glut of New Data Scientists” section of this blog by Vicki Boykis.

Applicants Focus on the Wrong Things

As I’ve combed through resumes, cover letters and LinkedIn messages for the past five years, I’ve noticed applicants consistently missing the mark on what will set them apart. They consistently point to technical ability:

                Technical skills (SQL, Python, R)

                Mathematical skills (Statistics, algorithms, modelling)

                Certifications (Data Science Bootcamp, vendor-specific courses)

Those things are all great, but they don’t differentiate you from the pack. Everyone has some nominal experience in these things, you likely don’t have experience in all the tools the company needs (What if they use Looker instead of Tableau?) and even if you didn’t much of this can be taught on the job.

When I interviewed at my current position, I wasn’t asked one technical question. When the Director of Analytics stopped to see if I had any questions, my first one was “Why aren’t you asking me any technical/SQL questions?”  I’ll never forget his response: “If you’re missing any technical skills, we’ll teach you.” Wow.

This seems counter-intuitive. Isn’t data analytics/data science more technical? Don’t you have to code?  Of course you do! But those aren’t the most sought after skills; they’re a means to an end.

What Top Applicants Demonstrate

Analysts that shine on applications and interviews show they can persuasively communicate complex ideas using data. The job of a data analysts is to work with a stakeholder to generate business value. That doesn’t happen through coding – that happens through understanding the business, understanding the problem (even if it’s not directly stated!), breaking that complex problem down and communicating what the data says to do. Technical ability is solely leveraged to get there.

This is in the realm of “soft skills” is learned quickly on-the-job and is tougher to gauge for someone with no experience. How effectively can you work with non-technical stakeholders? Will people like working with you? Can you distill an ambiguous question into an actionable insight?

Top applicants can point to experience showing they can handle these scenarios and that’s why they rise to the top.

Tough to Teach in Classes

Classes are unfortunately a poor place to learn and/or demonstrate critical analytics soft skills. Teachers ask you very precise questions and give you very precise datasets to see if you’ve understood explicit topics listed in the syllabus. In the real world, this isn’t how analytics works.

Sometimes you aren’t even told there’s a question. If you’re asked a question the person may mean an entirely different question. The data might not exist, or it might sort of exist, or you might need to make it yourself. Your presentations are to a potentially skeptical crowd who doesn’t care what methods you used to arrive at your conclusion.

You can see the pattern – it’s near impossible for a teacher to create this sort of ambiguous and dynamic setting in a classroom. Imagine not knowing what day a test was coming, or if there were even questions on the test, or if the questions on the test were the ones you were supposed to answer!

This stuff is learned on-the-job, hence the need for experience.

OK OK, I get it. What do I do?

Now we’re to the crux of the matter – is there anything to give yourself better odds at landing that first job?

Yes.  There’s one overarching tactic, with three options you can do today to gain experience that will make a difference in an application.

Option 0 – Network, network, network!

This applies to all three other tips. If you’re throwing your resume into the ether of hundreds of applicants, you’ll find less success than networking with the leverage of the tips below

Option 1 – Start doing analytics in your current job

This is where many of us (including me!) started. You know how there aren’t enough good analysts out there? Take advantage! That means your company needs data people. Your boss, or some other boss needs help. Discuss their data issues and see if you can take something solvable. Don’t overcomplicate this. Use Excel to start, it’s a great place to iterate and extremely flexible. Move from there – find a pain point someone has with data and try to solve it. Start small and build. This is phenomenally effective, and you can point to this experience when applying to jobs later on (or move to a data position at your current place!).

Option 2 – Work on a data project you are passionate about

 I see tips everywhere saying “take on a personal data project” but rarely see much helpful advice beyond that. My top recommendation is to think of a hobby or interest you have and create an end-to-end analysis. Do you love a particular sport? Try to predict something that will happen. Have a favorite hobby? Think of a dashboard you could create to display your time spent/skill improvement. The more interested you are in the data, the further this will take you and the more time you’ll put into it. The options here are endless, but should be regarding something you love.

This gets you experience across the entire analytics pipeline – finding/cleaning/enriching data, asking good questions, visualization. Tableau Public is a great way to publish your results and iterate. The options are endless, and you can demonstrate skill and passion in an interview pointing to a portfolio of data projects. It doesn’t matter what tools you use, though my only recommendation is SQL and some sort of viz tool be involved.

Option 3 – Take online courses to brush up on technical ability

I’ve spent a significant amount of time saying that technical ability won’t separate you. That’s true, but you do still need some technical ability or you may be disregarded as not technical enough. If you don’t know SQL at all, you can take some basics online as part of doing Option 2. Do some basics on Tableau or Python or whatever strikes your fancy. This certainly is the least helpful option for standing out, but it also is a prerequisite if you lack technical ability. Typically if you’re doing Options 1 and 2, you’ll end up needing to do this option anyway.

In Conclusion

Breaking into analytics isn’t easy. But there are methods to get past the Great Filter and get your first job. It’ll take hard work and some luck and the goal is attainable. Companies need passionate and smart people to make sense of their data, and you can step into that role. Make it happen!

Categories
analytics bi businessintelligence everythinganalytics

The Many Wandering Paths to Analytics

If we treated careers more like dating, nobody would settle down so quickly.

David Epstein in Range: Why Generalists Triumph in a Specialized World

I consistently receive the same questions from people seeking an Analytics career: What classes should I take? What certifications should I get? Should I learn SQL, Python or R?

Behind those questions there’s a consistent assumption: “There must be a clear path to an analytics career.”

I’m here to challenge that assumption. There isn’t one clear path to work in analytics – most of us got there through a winding, wandering series of career moves. My story is one of many – ask someone in Analytics and you’ll hear something similar.

Typical Wandering Path

(1) Get a college degree or other training – not super relevant
(2) Work for a while in some job non-analytics related
(3) Recognize interest in analytics
(4) Start doing basic analytics at work (ideally) or on own time
(5) Leverage that experience into first analytics job

I’ll call out each step as it happened in my career journey.

My Wandering Path

Initial Career (Years 1-4)

Coming out of college I shared the assumption that careers were linear. After all, life to that point was linear, so why wouldn’t careers be the same?

Except, my linear plans fell apart two days before my wedding in 2011. I’d studied International Economic Development (Step 1), interned in Latin America, become fluent in Spanish and was planning to move with my soon-to-be wife to Bolivia. In one phone call and several subsequent conversations, that potential life and career ended. I was sitting in a dead-end job I thought I’d be leaving and had to figure out Plan B.

At first it wasn’t obvious – what else should I do? I was a Customer Success Manager (Step 2) but didn’t really want to do that as a career. I’d worked in sales departments, but didn’t really want to be a salesperson. But then I had an epiphany – there was a part of each of my first few roles that I loved that never was part of my job description.

I was consistently making little analytics & reports (Step 4 – which ironically for me came before step 3!). I’d turn 2,000 customer emails into a digestible summary for the product team. I’d make Salesforce forecasts & dashboards for the executive team. I made a Google Sheet for my Rosetta Stone team to help management track & manage renewal rates for their teams. This stuff was fun! I liked it! (Step 3) But what now?

The Great Filter: Landing First Data Job

Have you heard of the concept of the ‘Great Filter’? It’s part of the Fermi Paradox, which ponders why there is no extraterrestrial life given the seeming high probability it should exist in the universe. Within the Fermi Paradox, it’s the step getting from non-living matter to living matter (abiogenesis). The Great Filter is a catchall for “it’s hard to get past this point.”

I argue there is a Great Filter for those trying to get into Analytics – getting your first job. In fact, I’m devoting my next blog to this topic, so consider this a lead in to next week.

Passing Through the Great Filter

I realized I had an uphill climb ahead. Perhaps this is where many of you are – how do you get a company to take a chance on you?

I asked lots of current analysts via informational interviews at local coffee shops. They all said “I got here via a pretty random series of events.” Sound familiar?

They gave me a breadcrumb trail, though: “You have to get enough experience together and communicate about it well enough to get someone to try you out.” Easier said than done, but I did have some experience already in my current role.

I applied everywhere. I was told by recruiters/HR multiple times “Hey, I guess you could be an analyst but I think your future is in sales based on your resume.” Leads fizzled out until one day I got a call.

The Meeting That Changed Everything

“Can you show up at the office in an hour? The CFO and SVP of Sales want to talk.”

I got that call from Jacob — the same Jacob here at DataDuel. He was working at Funko, a quickly-growing collectibles company north of Seattle. They didn’t have a position open yet, but there was interest in getting analytics going. Before going into the meeting, here is all I knew:

  • The position was planned to be part-time Analytics and part-time something else until analytics skills were proven
  • The position was planned to be a contract position (not great – I’d just bought my first house and wasn’t looking for a contract spot)
  • There would be minimal support since no data team existed, so a self-starter attitude was needed

Given those three bullet points, I had three goals going in:

  • Communicate my potential to be great at analytics if given the chance
  • Sell them that I was worth a shot as an employee & not a contractor
  • Demonstrate self-starter attitude to analytics from previous roles

I quickly threw on a dress shirt, re-learned how to tie a tie on YouTube and flew out to the car.

The rest is history and full of the content I’ll fill this weekly series with. The conversation went really well and they decided to take a chance on me a couple weeks later (Step 5!). While I got a full-time position, I took a 10% pay cut because I needed to prove myself. I knew the temporary sacrifice would be worth it – I just needed my first position to get past the Great Filter.

In Conclusion

There’s no one path to analytics – there are many. I’ve used my path as an anecdote for the infinite options out there.

The general path, though, is to start doing some analytics in any fashion you can, and leverage that experience to get your first position. It isn’t easy – there’s a Great Filter out there which prevents many from getting in.

Come back next week and I’ll dive into the Analytics Great Filter in more detail, and provide some practical options of how to overcome it.

Categories
analytics bi businessintelligence everythinganalytics

New Weekly Series: Everything Analytics

Do you enjoy working with data in your current role? Are you interested in a Data Analytics career? Are you currently a Data Analyst?

Good news! This weekly series is for you. It’ll cover all sorts of topics within analytics, including advice for aspiring analysts, best practices, key skills/tools and industry updates.

Initial blog topics include:

  • The Many Wandering Paths to Analytics
  • Analytics Job/Role Types
  • Key Skill Sets for Analysts
  • Visualization Best Practices
  • Measuring Success of Analysts
  • How to Prioritize Your Work Backlog
  • …and more!

Much of this will be written from my perspective as an Analyst. There are other perspectives out there for unique positions like Data Scientists and Data Engineering, and while I’ll touch on those regularly (and will write an entire post on the difference between those roles), the focus here will be Data Analysts.

See you in a week!