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How I Landed a Job As a Product Analyst

An account of my job search in the field of analytics during Covid-19

01 Feb 2021 by Nishant Mohan


I did my postgraduate studies in computer science with specialization in data science. After I completed the course in 2020, it was time to look for jobs. Though I had relevant professional experience in analytics, searching for jobs was new for me because I landed my earlier job fairly quickly during campus placements after graduate studies, and therefore I had never really searched for jobs nor given many interviews. Adding to my woes, the covid-19 had largely weakened the job market.

After hundreds of unsuccessful applications, I finally secured a great job. In this article, I talk about what I think helped in retrospect. If you are reading this, I suppose you are looking for jobs too. Hope I can be of help!

Preparation Before Applications Began

There are four things I gave attention to-

  1. CV- They say that you should change your CV according to the job description. I did too. I took help of articles that suggest how to build a great CV. With every application, my CV improved. I even received appreciation for my CV from two or three interviewers.
  2. GitHub- There have been interviews where I actually showed my GitHub repos via screen sharing. It’s important you have a great Readme for your projects. Not everyone would look at your GitHub, but if they do, they should be able to understand what, why, and how of your projects through your Readme.
  3. Portfolio- I designed my personal website, which had an overview of my professional career, my blogs, and my projects. Not because I felt it was needed, but because I saw someone else’s, and I was thrilled to have my own. Took only a day or two build but helped enormously when I reached out to someone on LinkedIn. Always think from the perspective of the other person. If someone was reaching out to you, would you rather download their CV, or click on a link?
  4. Blog- Getting a great job had been on my mind since half my degree was over. One day, I decided to write about one of my projects on medium. I discovered that I liked writing. I felt like I was interacting with the readers face to face. It felt good to be explaining about a project that I was proud of. It felt good to see my name on a blogging website. But at the same time, I knew this might help me build a profile that would be different than my peers.

Proud fact #1- I got contacted by a startup because its founder read one of my articles and it related to their business problem.

Proud fact #2- A very accomplished data scientist reached out to me on LinkedIn and appreciated my article that cited his paper and explained his algorithm.

The Interview Process

Out of the hundreds of applications that I filled for data analysts, product analysts, and data scientists, I got replies from only a couple of them. Though I had good prior analytics experience, the fight was tough because the job market was cold. However, there were still some takers. All the interview processes were more or less like this- a recruiter call, a technical take-home assessment, hiring manager interview and finally a series of interviews with the team which included product managers, analysts, directors. Let’s talk about each one of them in detail.

The Recruiter Call

All the interviews begin with an introduction. I prepared a detailed introduction, which specifically addressed these points-

  • Where I am currently in my career phase- 1-2 lines stating that I have recently completed master’s studies, and have these many years of relevant work experience
  • An overview of what I did in the previous role- My responsibilities, nature of work and end objectives
  • One liner each about some specific projects that highlighted my nature of work- Forming and analyzing customer journey funnels, a/b testing and customer segmentation for marketing campaigns, statistical hypothesis testing for deeper analyses, predicting costs for incoming customers and so on.
  • About postgrad- How and why it was necessary to pursue further studies, what I learned during it, about master’s thesis, if relevant.
  • What’s next? Now that everything is done, what am I looking for? How does this role and company fit into my future plans? Do the dots connect?

In my experience, if you can explain to the recruiter how you fit the job description, you will get to the next round. Not a big deal. Be prepared for the obvious. For example, I often got asked during analytics interviews, “your academics have been in data science, why do you want an analytics job?”. Have your arrow ready.

The recruiters generally discuss your rough salary expectations at this stage itself. So, do your research beforehand. Do remember to ask them about the stages involved in the process.

The Technical Assessment

Companies may ask you to perform an analysis on their own data, or on a public dataset. Often, a set of questions is given, which need to be answered using the given data. There are no shortcuts here. Generally, you get one week to complete an assessment. Bear in mind the business objective of the task. Answer the questions and provide your solutions from a strategic perspective. Think big.

If I differentiate between the assessments that I cleared and the ones I didn’t, I can only tell one difference- I did not pour my heart into the ones I didn’t clear. Be mindful, companies do not expect you to provide a shining, production ready solution for the problem. Rather, they can sense the effort. For the ones that I cleared, I did not just submit my codes, but I also made cool PowerPoint presentations explaining my methodologies, along with what more could have been done.

The Hiring Manager Round

The hiring manager interviews are part technical and part experience based. There may be three components in this interview-

  • Assessment Review- Revise your assessment submission. Think why you took the approach you did. Think how else you could have done it. Think what assumptions you made about the data. Think what else would you do if you had more time. Think!
  • Behavioral Questions- In all my interviews at this stage, I was asked situation-based and behavioral questions. Some examples are “Which is the most interesting project you worked on?”, or “Who were your stakeholders? How did you collaborate with them?”, or “Tell me about a time when you had to stand up to your manager”. Expect that the interviewer will go dive deep in whichever project you talk about. Therefore, it’s important to recollect some interesting stories from your experience before the interview. Use STAR methodology to answer, quantify your results, if possible. Phrase your stories such that the listener gets hooked. Show your excitement while narrating your story; if you are not excited, who else would be? Imagine if you were hearing this story from someone else, would you have liked it?
  • Questions?- Hiring managers also tell about the role and teams in detail. It’s important to ask good questions. It shows that you are a curious soul. Ask questions that may help you later in deciding if you get more than one offer simultaneously. Ask what the career path is for this role. Ask who are the stakeholders for this role. Ask how much flexibility and freedom is given to do day-to-day tasks. Ask!

The Final Interview(s)

There could be one or more back-to-back interviews with your prospective team. Their focus is to gauge your product mindset, proficiency with data, and team fit.

  • Product Mindset- Some questions to gauge your understanding of how a product works. How would you, as an analyst, define the KPIs? Which KPIs did you measure in your last role? In one of the interviews, they suggested me an A/B test with regard to their product and asked my views on it- does the test make sense? Will it be successful? What are its assumptions? Which one is the control group? How would I measure its success? Which KPIs? How to decide on a KPI? Which statistical test? What is the importance of measuring product performance?
  • Data Proficiency- What are the technical challenges you’ve solved with data? Have some real examples to share. Formulate your experiences in the form of a story. This may entail some live coding as well. Initially, live coding used to give me the heebie-jeebies. But if you practice well-enough (try codility and hacker-rank), you should be good. Remember, it’s okay to use google even in live-coding sessions. Maybe ask your interviewer before you do.
  • Team Fit- Consider how you partner with business or product leaders. What’s important to share with business partners? How do you approach this? Have you worked in collaboration with other teams? How did you communicate? Did you ever have a situation when you disagreed with your team? Can you explain technical stuff to non-technical people? Here’s my favorite one- Explain A/B testing as if telling it to your mom!


While there is no set pattern for the number or order of interviews in the field of analytics, I have tried to make out a pattern based on my little experience of job hunting. It’s always important to know about the company you’re interviewing with. For all the companies I had hots for, I made sure that I know about the product in depth. I read online as much as possible. These days many companies have their own blogs, which give insight into their tech stack and core values. I found them useful. During the interviews, I would intelligently slip in such information. For example, I read on a company’s tech blog that they use a particular tool, say X, for conducting A/B tests. When asking questions towards the end of an interview, I asked if there is an active collaboration with the tech team of X. How does the integration of X with this company’s product work? Such questions do get noticed, as was evident to me in feedback later. They hint at your passion for the product and the company.

I believe that Spotify would rather hire a musician than a chef, if they are otherwise similar. Don’t you think so!?

Hope this helps!


  • Connect with me on LinkedIn!
  • Check out some of my cool projects on GitHub!

Originally published here.

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