Sometimes we forget the humans behind the tech in our ever busy world. DSF is fortunate enough to know some incredible tech leaders across the world and has the privilege of hearing them present at our events. That being said, our Speaker Spotlight sets the stage to get to know our speakers on a more personal level and connect them with our growing community. Read the mini interview below!
A bit about Raquel
Raquel is a Senior Data Scientist at Trustpilot where she leads data science initiatives in a cross functional team. Some of her work includes improving the search engine, recommendation engines, and consumer representations to enable personalisation. She also champions best coding practices among the Data Science and Analytics team. Previously she worked at Culture Trip and Seatfrog. She holds a PhD in Applied Mathematics from Imperial college London.
How did you start out in your tech career?
I started my career in tech by joining Faculty’s fellowship programme which supports people from academia (PhDs, Postdocs) to transition into a career in Data Science. It is a 2 month programme during which people get to work on a real project with a company as a consultant whilst receiving training in both Data Science and business soft skills. I got my first job as a Data Scientist after that and I cannot recommend it enough. Another great advantage is the amazing network you build.
What are the signs of success in your field?
I believe that success in Data Science comes down to the impact on the consumers and clients (internal or external) of a business. This ultimately translates to the impact the team has in the organisation. Proof of concepts and MVPs are super helpful to get buy-in from the organisation, but if in the end it does not get translated to a deployed solution the project is not successful. Not to mention it is incredibly frustrating for Data Scientists.
Something I observed that could be identified as a tipping point for the DS function in an organisation is when Data Scientists have to do less pitching to propose product enhancements and Product Managers actively seek us out to solve their most difficult tasks. There is a clear shift in the roadmap initiatives which will be more populated by products powered by Data Science models.
What is the best and worst thing about your job role?
The best thing is how interesting it is and how smart everyone is around you. There is never a shortage of challenging problems that can be approached with Data Science. As the field is rapidly changing, it is amazing to see the new tools that become available. It requires a lot of creativity to imagine new and better solutions with these tools.
The thing I least enjoy is how in some organisations the role of a Data Scientist is not well defined, which means we might end working on tasks better suited for other Data related roles, example, Analytics, Engineering, MLOps, etc. This makes it harder for Data Scientists to really specialise and focus on what makes them most valuable.
What can you advise someone just starting out to be successful?
My advice is always start with the simplest possible solution. I know we are often tempted to use the latest state of the art model, but the reality is that it might be quite hard to deploy, especially on V1 of the product. It would require the business to have the willingness to invest a lot of resources before seeing any results. The risk is quite high too if the project fails. My preferred approach is to start simple (I am talking linear regression simple), deploy that and iterate. It is much easier to justify investing resources as soon as something starts showing positive outcomes.
How do you switch off?
I really like going for a walk after finishing work when I am WFH. The boundary between work and home has become a bit messy, it is important to have a healthy routine to separate it. Leaving the house helps me get into a different mindset.
What advice would you give your younger self?
It is ok not to know exactly everything we want to achieve in life. It is good to have a rough idea of what we want to optimise for, but it is important to keep an open mind and pay attention to where the opportunities are, especially during an ever increasingly fast changing world.
What is next for you?
Not in the near future, but one of my long term plans is to start my own business. Until then I am focused on getting as much valuable experience as I possibly can and having fun building DS models to power amazing products.
If you could do another job now, what would you do? Why?
I would like to be an Angel Investor if I had the capital for it. I would love to support growing businesses whose people and mission I believe in.
What are your top 5 predictions in tech for the next 5 years?
Lots of new tools and applications built on top of generative AI.
Massive increase in efficiency. We can now use tools that help us write code, sure we can write it too, but it is so much faster if we only tweak it a bit. I think of it like before the invention of the calculator, people could do maths by hand, but it was so time consuming. People want to focus on the solution not on the implementation.
Most successful people in a creative space will be early adopters of generative AI. I don’t think it will replace as many jobs as some predict, but it will make content generation faster. Example, ask for a specific style in an image, video or text and replicate it on a large scale, make it personalised, etc.
Disruption of more traditional businesses that lag behind in using technology. I’m thinking about insurance, legal, engineering, etc.
Companies that do not adapt and integrate generative AI in their offering will not survive. When we had the transformation to digital companies like Amazon, Netflix, etc. succeeded because they adapted. I expect the same will happen here. The expectations of consumers will continue to change.
Watch Raquel’s session at the Data Science Festival here.
Thank you to all our wonderful speakers for taking part in our Speaker Spotlight!
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