Harnessing the power of data: Kami Charles and Henry Ling

Read this blog to learn more about Kami and Henry's career journeys and what it’s like working within the ever-evolving world of data science and machine learning.

We sat down with UCL alumni and friends Henry Ling and Kami Charles who both currently work on the same team as Data Scientists at Tata Consultancy Services (TCS). Read on to learn more about their career journeys and what it’s like working within the ever-evolving world of data science and machine learning.

What did you study at UCL? And what did you do outside of your studies?

Henry:  My background is in Mathematics which I completed before doing a Master’s in Data Science and Machine Learning at UCL. During my MSc, I worked on projects that involved developing a Hate Speech Detection Classifier as well as building a model for enhancing MRI images to make it easier for practitioners to identify diseases. Outside of my studies, I am a keen coder, lacrosse player and D&D enthusiast.

Kami: I also completed my Bachelor's degree in Mathematics before moving onto my Master’s in Data Science and Machine Learning at UCL. During my Master’s I worked on many small projects such as a multi-class sentiment classifier and a denoising autoencoder. In addition to this, I also built a simulated robotic system that learnt to pick up objects through Reinforcement Learning. In fact, Henry and I got to work together on some projects which allowed us to not only develop the foundational theoretical knowledge, but also practically apply this new-found knowledge to create some really interesting models! In my spare time, I love practising taekwondo, going to the gym and playing Osu.

What does your career path look like? How did you get from UCL to where you are today?

Henry: After completing my undergraduate studies in Mathematics I wanted to pursue a career that would use those analytical and problem-solving skills. It was at this point that I started to hear about the rise of data and its extraordinary impact on the world today. Researching this further I developed a strong interest in data science and how you can use data to build exciting models to predict trends, understand language and detect objects in videos. Driven by this I started to build my python coding skills and take on my Master’s at UCL. With the knowledge gained in my masters I was then able to start to build a career in data science. Currently, I am a Data Scientist at TCS, helping to develop interesting modes to help companies in the banking and financial sector.

Kami: During my 2^nd year of my undergrad in Mathematics, I had a module called Numerical and Computational Methods that involved some Python. It was the first time I had been exposed to a programming language that I enjoyed as previously R Statistics hadn’t clicked as easily. I then began to research the types of roles that involve mathematical theory and python and stumbled across data scientists and the idea of machine learning which I was very intrigued by. This is what ended up shaping my next steps, aiming towards a data science role, where I work on the same team at TCS with Henry.

Have you both been able to apply any of the skills and knowledge you gained from your degree to your role?

Henry: The Master’s course at UCL gave us invaluable skills which we have taken with us to the working world. The course teaching us the key building blocks of statistical machine learning as well as deep learning. This building blocks acting as a base for learning new techniques and understanding current state of the art.  However, our knowledge did not stop there. TCS have given us excellent opportunities to develop our knowledge in more specific areas of data science as well as build and widen our horizons into web development, such as Git version controlling and cloud computing.

What is the biggest lesson you’ve learned from your careers so far?

Kami: The biggest lesson we have both learnt so far is that there is always more to learn. Due to the ever-changing nature of the industry, you will always be learning new things and developing the skills you already have. Our best advice would be to seek out opportunities and look for ways of expanding your knowledge set.

What does a normal working day look like for the both of you?

Henry: Our days mostly start off at home at our desks since hybrid working has become the new norm. The first 30 minutes are usually spent checking emails and writing a to do list for the day. Currently the bulk of our days are spent researching and understanding methods suitable for tackling the project we have been assigned to, as well as annotating data for use in our models. It is also important to keep a running work diary so we can keep track of what we have done and the progress we have made. Our company is very flexible and so we often end up working our own hours from 8-4 or 9-5.

What is the most enjoyable part of your work? Equally, what is the most challenging part of your work?

Henry: We would say the biggest reward is that our role is intellectually stimulating. The project we have been assigned to involves a lot of forward thinking and coming up with multiple solutions to try and test out. Since the methods we need to use and problems we need to solve are cutting edge and haven’t really been solved before, it requires answering many challenging questions to get to a solution, but it also keeps things compelling!

The most challenging part is explaining to non-technical people that a task we have been given isn’t as simple as they think it is and has a lot of different, complex aspects to it.

What are currently the most topical issues that you see happening in the world of data science and machine learning?

Kami: Machine learning models shape our financial decisions, the advertisements we see and the medical treatments we undergo, however, there are many difficult challenges which the industry faces. Privacy has become an extremely important issue causing us to take care and think twice about using different pieces of data. Bias within datasets is also paramount for ensuring that the models you produce are fair and representative. Not understanding and monitoring your datasets can lead to negative repercussions for your company. Another topical issue within the industry is accountability, producing explainable models is important when the outcome of its predictions affect people's lives and well-being. However, these industry challenges are part of what makes the job exciting. We will often have to adapt and think outside the box to solve these issues.

And finally, what advice would you give to students and recent graduates who are looking to move into your areas of work?

Henry: Take what you’ve learnt during your studies and create projects based on areas you are interested in! It’s a great way to demonstrate your skills and expertise in a more enjoyable way. It’s also a fantastic display of your passion for the area without explicitly saying you are passionate.

Kami: My advice would be to do your research on companies and their corporate values when you start applying for jobs. TCS specifically are doing a lot of positive things to encourage more people into the field. Setting up workshops and bursaries for students from diverse backgrounds as well as creating a strong support network for people within the company from different backgrounds. Our team made a special effort to try and get some young freshers to try and encourage new lines of thinking and development within the team. Also, as a company TCS employs around 33% female employees which is miles ahead the tech industry average which stands at just 19%.