Data Scientist Review

Role greatly depends on placement allocation. In Business Operations, placement rotations currently happen every year. In my current placement, my work involves process optimisation, business analytics and stakeholder management.

I have learned how to use PowerBI and developed my Python skills. My manager is very supportive of my desire to learn, but I have had to stress my desire to build new skills in order to be provided an opportunity to do so.

The programme currently feels a lot less technical than I expected, and doesn't quite feel like Data Science, but this is completely down to my placement allocation into an engagement-focused team. There are still opportunities to do technical work, but I have had to search for them myself.

The programme is structured well.

My manager is very supportive and I feel comfortable discussing any concerns I have with him.

The training provider is ok. This is my first time doing a course with online lectures and I think I prefer in-person learning, but this is just a personal preference. No major concerns or issues. Some of the lectures can feel rushed because we have limited time each week.

At the moment, there isn't much link between the two, as my role in work isn't very technical. I expect this to improve once I start working on a more technical role.

There are plenty of activities, but I haven't participated in any.

No

For a Data Science career, I think Unilever may be a good option, but only if you are working in the R&D department. It would be a lot easier to develop technical skills in R&D. Business Operations feels like a regular corporate job.

For a Data Science apprenticeship, you should aim to be placed in R&D so that you have more opportunities to develop technical skills that will enable you to land a full time Data Science role after completing the scheme.