What does a typical day & future options look as a data scientist or business analyst?


For all those who are working as Analyst or expert Data Scientists,

  • What is your typical day and what is the kind of work you deal with in the office?
  • What are the available future options? and
  • What do you expect to be doing in 10 years?

Thank You.



I have been in analytics industry for almost 10 years now. You can look at my profile here.

But to be honest, I never planned for 10 years ahead - it is too long to plan for in a technical domain like analytics. Even today, when I am evangelizing Analytics Vidhya, I don’t know how things would be in 10 years from now. The landscape changes more quickly that you imagine and next 10 years have a lot in store. So, I’ll answer the first 2 questions from perspective of an analyst and a manager (and not as an entrepreneur). I will also give some view on how the past decade has been for me.

As an analyst / analytics professional working in a captive setup:

I am only answering this form the point of view of an analyst / data scientist in a captive unit. There can be a few difference for people working as a consultant.

  • Typical day: My typical day would start with a coffee with the team and plan for the day. While working in this setup, there used to be a lot of collaborative project work. So, we used to all sip our coffee, discuss the progress from last day and then plan for the day. Once that happens, you would typically spend 2 - 3 hours working through a particular project (your main project). These could be brainstorming with the team in initial phases or slicing and dicing data on your laptop. By this time, my tummy would start to grumble and some team mate or the other would ask for lunch. Typically, the team would go out once in 15 days / month for a lunch outside. Lunch would be followed by a quick walk outside. Typically, second half would have a few meetings, pitching new projects to potential internal customers and progress / monitoring from some of the past projects, which might have happened. If not, brainstorming and the work on project would continue. Typically, you will present the progress of your project to your customers every week or 2. You will take their inputs on what you are seeing from data. The remaining evening would typically be spent on creating presentations for the meetings later in the week. Once your mind is tired of number crunching / coding / creating presentation, you pick up your bag, say bye to your team and call it a day!
    As you go up the hierarchy, the time spent in meetings and stakeholder management typically increases. You will be spearheading several projects and mentoring several analysts in 4 - 7 years.
  • Future options: This pretty much depends on what you want. You could continue to grow as a data scientist -> Manager -> Chief Data Scientist. Because of your structured thinking, you can also look at entering into Strategy roles at any point in time. That path might lead you to a Chief Strategy Officer and hopefully a CEO some day. You could also look around tonnes of problems around you and take a plunge as an entrepreneur to solve them. Startups and Big Data setups could be an interesting option as well.

My experience over last 10 years:

I think I was lucky to get a chance to work with Capital One straight after my college. I didn’t know a lot about analytics when I took up the role. I just went ahead with it based on recommendations from some of my seniors. I find students today lot more informed about their choices (you are one such example :smile: ).

While at Capital One, I worked across several roles and learnt different tricks of the trade. When I joined Capital One, I did not even knew how to calculate confidence intervals and just knew basic excel. In a matter of ~4 years, I learnt Advanced Excel, SAS, SQL, Oracle, Google Analytics, Omniture, created DOE tests and inferred them, built predictive models and saw them implemented.

Since I wanted to be in India in long term and I came across this opportunity where Aviva wanted to setup its analytics team, I joined Aviva along with 2 ex-colleagues from Capital One. During first year at Aviva, we worked on some problem in individual capacity, proved the value of analytics to business and then scaled up. By the time I left Aviva, I was leading a team of ~20 analysts / BI professionals. In these 4 years, I learnt a lot on challenges in starting something from scratch and how to manage and influence stakeholders. My role had become like a manager, who was either pitching for new projects, monitoring the progress of previous projects or managing HR issues with in team.

By this time, I started feeling I was getting behind the curve in technical learning, so I started reading about data science and machine learning and started blogging on Analytics Vidhya. Over time, my work at Analytics Vidhya became more interesting that my work at Aviva and I took the plunge to do it full time. Today, I spend a good amount of time reading and learning new things, solving unsolved problems through data and evangelizing world’s largest analytics community. I feel damn excited about what data science has in store for future. Whether that happens in 5 years or 10, it doesn’t matter. It is the journey I love!



Thank you very much @kunal for the detailed and helpful explanation!