What’s your career journey been like up until now? 

Prior to joining Sky, I had a brief stint as ML researcher for a defence company in applying deep learning CNN to digital surface models. Then I worked in clickstream analytics at a company that specialised in optimising website experience, by identifying user behaviour patterns. Now at Sky, I work on problems that are more closely related to customers and their experience.

Before adorning the hat of a data scientist through a Master’s degree in Data Science, I have been a business analyst, QA and project manager with energy trading, utility, financial services and retail organisations.

What does a typical day working here look like for you?

Most of my time is spent in wrangling data, getting analysis/models to work at big data scale on GCP and, learning technologies that I had not extensively used previously or catch up with those that move so fast.

When not caught up with my work, you will find me ambling by the extensive food options in Osterley office, most often with my colleagues.

What’s been your proudest moment since working here?

I can’t mention any particular moment, but I am certainly thrilled when I get some of the complex models running at scale on new technologies. I am also extremely happy that I am part of the team that applies data science for business decisions.

What’s the most enjoyable thing about your job?

I am doing what I wanted to do in data science. I love the variety of tech stack and abundance of data that are available to work with.

It’s not a flimflam that Sky has been a very good employer. I also enjoy the beautiful office and on-campus facilities that add a lot of convenience to day-to-day life.

Describe your work/life balance?

Longer hours are initially needed to adopt to new technologies but the flexibility helps to manage work/life balance.

What advice would you give to others trying to develop their career?

I realised that unicorn data scientist is not a myth but very important to be productive.

Good skills in Maths/Stats, expertise in at least one high level languages, ease in working with Big Data technologies and proficiency in SQL are all required to get started even at early stages. This can be difficult and time consuming but does not matter as long as you remain stoic and do not stop.

An interesting fact/something no one else knows about you?

I gave up a long career in software QA for 12years to move into data science.