One Day in the Life of two Mathematicians Juggling with Data

One Day in the Life of two Mathematicians Juggling with Data

by Mara Hermann & Marisa Mohr

The daily routine of a mathematician in the field of Data Management & Analytics can be diverse: Data collection, preparation and analysis, the design of artificial intelligence (AI) models, and much more. The opportunities to get involved in a data project are usually not limited to one’s own field. We, Mara (Senior Big Data Scientist) and Marisa (Senior Machine Learning Engineer), are two mathematicians who juggle data in a variety of ways every day. In this blog post, we describe what a day as a data juggler is like and how we use mathematics in our everyday lives.

If you study maths, you are faced with a wide range of possible career paths. But you should definitely take a look at the field of data management & analytics – not just because the Harvard Business Review called the data scientist’s profession the sexiest job of the 21st century [1]. In recent years, many specialised job titles have emerged, for example “Data Engineer”, “(Big) Data Scientist” and “Machine Learning (ML) Engineer”. However, they all have the same aim: to process data in such a way that useful information can be extracted (learned) from it and computers can act intelligently based on this knowledge. In particular, working with and implementing AI algorithms requires more than just AI experts – it’s a team sport. Regardless of their job title, it takes many different specialists working together as a team and complementing each other. Other areas of computer science such as database management or software engineering are also becoming increasingly important. 

Marisa, what is your role as an ML engineer in the team and when do you still use maths?

Due to the above-mentioned diversity and the numerous connections to other team members, it is difficult to describe a typical day of an ML engineer because every day is characterized by new challenges – fortunately. However, even with the most complex challenges, our mathematical-analytical approach does not make us despair.

The mathematical modelling of data in a learning algorithm, be it through a slightly more applied, specialised linear regression, or through a fancy artificial neural network, usually takes up no more than the last 5-10% of a whole data project. For a prediction to work really well, the end-to-end idea is crucial. Where does the data actually come from? And what data do I need to arrive at a valid result? Do I have the right data? Can I get to more profitable data, or do I have to change the prediction goal? It’s crucial to understand the big picture. After all, you need exactly the data that fits the problem you want to solve. 

All of AI […] has a proof-of-concept-to-production gap. […] The full cycle of a machine learning project is not just modeling. It is finding the right data, deploying it, monitoring it, feeding data back, showing safety — doing all the things that need to be done to be deployed.

Andrew Ng [2]

In general, an ML engineer is a person who helps deploy machine learning or artificial intelligence algorithms in a productive environment so that they can be used in the day-to-day business without difficulty. That sounds like a lot of infrastructure operations and software engineering, and yes, that can be a big part of an ML engineer’s job. You have to understand the existing IT landscapes and systems at the customer level to decide how to build a pipeline in those existing systems between the data and the output of a prediction, and how to deploy everything at the end. But as mentioned before, AI is a team sport. Of course, as an ML engineer, I’m not the specialist in everything, but it’s important to stay on top of everything.

Marisa Mohr

Now, how much mathematics is needed in this interdisciplinary field as an ML engineer primarily depends on the level and interest of the individual in the mathematical-statistical techniques that are being used. There is this type of ML engineer who spends all day building infrastructures or programming software to make an intelligent algorithm run productively in the client system. This kind of ML engineer is certainly more influenced by computer science than I am as a mathematician. I admire that, but I could never get lost in coding, and the good thing about being an ML engineer is that you don’t have to. The profession is so multi-faceted and multi-dimensional that everyone can follow their own passion and take their personal role in the team – with the bonus of dabbling in other roles every now and then.

As a mathematician, I have taken on various roles over the years. During a project phase, I often take on the role of a general strategist or project manager, ensuring that the team follows the same vision to bring together input and intelligent output in the productive environment. Then, when data modelling specialists are required in the project, I have the opportunity to follow my mathematical passion in the form of smaller data explorations and visualizations, through the evaluation of mathematical relationships in the data, to the selection and training of learning algorithms. The latter also includes consideration of accuracy, training time, model complexity, number of parameters, and number of features. In addition, parameter settings and validation strategies have to be selected, underfitting and overfitting have to be identified by understanding the bias-variance trade-off, and confidence intervals have to be estimated. A deep dive into maths for ML can be found on Medium [3]. As a mathematical minded ML engineer, my role can therefore be similar to that of a data scientist from time to time.

This role change and diversity is what I love about working as an ML engineer, or working in a data project team in general. Another ML engineer could certainly take many more technical roles, especially when it comes to gathering the appropriate data without which no ML or AI model works. And that’s where Mara comes in.

Mara, what do you do all day as a data engineer and when do you still use mathematics?

After my studies in mathematics, I started working as a data scientist for an IT company. When I applied for the job, I was asked in the interview what title I would prefer: data engineer or data scientist. At the time, I was convinced that the latter was the only reasonable choice for a mathematician like me. Even during my studies, I was a working student in the fields of data science and in addition to that, I also attended lectures on data mining, neural networks and other related topics. 

The connections between mathematics and data science are numerous – in fact, data science is mainly the application of mathematical models to various use cases. And I wish this fact would be taught  more often and more emphatically at university.

Have you ever wondered what all that mathematical theory is good for? If you are a mathematics student – have you ever been frustrated about all the types of matrix factorizations one has to learn in numerical mathematics? Or perhaps you are a high-school graduate contemplating the high art of analysis and algebra but you fear it will end in nothing?

I can soothe you: The use cases for mathematics and its theories are boundless. 

Mara Hermann

One of my favourite examples that I encountered during my job as a working student are recommender systems. A great introductory article on this topic can be found on Medium in which recommender systems are defined as “algorithms aimed at suggesting relevant items to users” [4]. Those items could be for instance products in an online shop or movies on a streaming platform. The interaction between items and users can be represented by a sparse matrix where each entry describes e.g. how a user rated a specific movie or if a user bought a given product. One approach to retrieve information and learn recommendations from this matrix is to decompose it into two smaller and denser matrices, the so-called matrix factorization. One matrix then describes the user representation and the other one the item representation – a great illustration of how a mathematical framework can be used in practise, just to name one example. Also other mathematical methods find use in the theory of recommender systems.

Now I fancied about how various and “sexy” [1] the applications of pure (and sometimes dry) mathematics in data science can be. But if you read this article carefully, you may have noticed that I wasn’t asked about my work as a data scientist but as a data engineer. Why?

As already mentioned, working on AI or – generally speaking – a data project is a team sport and in this course you also get in touch with other roles and switch positions from time to time. With my mathematical background I always had great respect for the role of a data engineer which I thought would be reserved for “real” programmers with an IT background. In the beginning of my studies I wouldn’t have thought that I would ever be interested in coding and, like Marisa, I will probably never be as much into programming as someone who studied computer science. But data engineering is so much more than sitting in front of the laptop, producing green letters on a black screen while typing at the speed of light.

The “unsexy” sibling of data science sure inherits more aspects from computer science than from mathematics [5]. As a data engineer, one designs, implements and monitors data pipelines which may feed a Data Scientist’s ML models. Additionally, data storage and quality are a huge part of the cake. Programming skills and willingness to permanently learn new technologies are indispensable in this job.

With this role description in mind, it’s true that you don’t necessarily need maths for being a data engineer. But that doesn’t mean that mathematicians can’t be good or even excellent data engineers at all. Their education entails a lot more than knowledge in algebra, analysis and many other subjects. It is often said that mathematics and philosophy are closely interrelated, some universities like Oxford even offer lectures combining both disciplines [6]. Even without attending such a course, a mathematics student acquires a lot of soft skills which are basic tools in the everyday life of a data engineer: One has to handle complex systems consisting of different data sources connected through various pipelines. With logical and analytical thinking one can better understand and design ETL (extract, transform and load) processes. Thoroughness and checking for accuracy are key to monitoring data pipelines and ensuring high data quality. Resilience, deduction and reasoning are of great help during performance tuning or debugging data pipelines. With some of these capabilities in your tool kit you have a great foundation for the role of a data engineer, practical experience comes with time. 

Thus, the opportunities for a mathematician in the data sector are broad. Different types of people and skills are required and there are numerous further training possibilities. Also, data projects can be very diverse, since data is everywhere: e-commerce, food and fashion retail, logistics, mobility, smart buildings,… One can always find a use case which fits one’s taste. I can definitely recommend taking the chance and gaining an insight into this branch. 

Regardless of which field of study or career path you choose, I can only encourage you to look beyond the horizon and also get a taste of other roles and fields than the ones you are already familiar with. Be it positive or negative, it will be a learning experience for you. And you will be an enrichment for every team if you can think out of the box.








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Paola Console

Paola Console

Born in Taranto, Italy • Birth year 1983 • Studied Mathematics at Università del Salento in Lecce, Italy – PhD at Université de Genève • Highest Degree PhD in Mathematics • Lives in Rome, Italy • Occupation Data Scientist at Enel

I was never good at math until high school. When I was a child, I loved spending my time reading and writing rhyming poems, so everyone in my family was sure my path would have had something to do with liberal arts. For this reason, they were really surprised (and probably worried) to hear I decided to start scientific studies in high school: for me it was a challenge, but I thought that by doing this I would have had a more complete education. There, I met a teacher who changed my life by starting to show math to me as a sequence of logical steps. I began finding it funny, logical, and telling everybody that to me, doing math exercises was comparable to playing crosswords.

After high school, it was logical for me to then start my studies in math in academia, with the idea to become a teacher. But in the end, I decided to complete my studies with a PhD in numerical analysis in Geneva, where I could also study different languages and meet people with different stories and backgrounds.

I really missed my country, my habits, my family, my friends, and therefore coming back home was a fundamental step to being happy in my life.

All the experiences I had while pursuing my PhD made me realize that I loved studying math, but that I prefer to apply it rather than develop new methods and proofs and, furthermore, that living in Italy was fundamental to me: I really missed my country, my habits, my family, my friends, and therefore coming back home was a fundamental step to being happy in life. I then decided to accept a postdoc position in neuroscience in Rome. I loved this job, but it was always meant to be a smooth transition towards the corporate world, where I would start to apply what I love to something more concrete by learning about machine learning and data science.

This experience helped me greatly in landing my current job, about six months after the end of my postdoc. I now work as a data scientist at Enel, one of the biggest private renewable energy companies in the world, in a huge group of data scientists that supports all the businesses and internal service functions, like procurement, in the company. My first projects consisted in applying machine learning techniques to detect faults in power plants, and I was very happy to finally see a real-world application for all my studies. Then I started to develop algorithms for the procurement field and now my main activity is undertaking a huge initiative to forecast the company’s income statement to support management decisions.

For all these reasons, when I think about my path, I am very happy about it, because it seems like I could, in the end, integrate all the different souls I had in my life (…)

What I really love about my current job is that it is based on applying math to the real world, but it is also really focused on relationships. Besides the modeling activities we carry out, I am also coordinating a small group of colleagues and I am involved in many other activities to spread data culture throughout the company with education and communication projects. One of the projects I am most proud of is the creation and the organization of an upskilling program called “Data School”, in which my team provides courses on topics related to data to colleagues of all areas. I think that engaging with people on topics related to data is a fundamental step to collaborate with them and support the data-driven transformation that is the main mission of my team. 

For all these reasons, when I think about my path, I am very happy about it, because it seems like I could, in the end, integrate all the different souls I had in my life: the little girl writing poems, the student that wanted to be a teacher, and the rigorous mathematician.

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Ellese Cotterill

Ellese Cotterill

Born in Newcastle, Australia • Studied Advanced Mathematics at the University of New South Wales in Sydney, Australia • Highest Degree PhD in Computational Neuroscience from Cambridge University, UK • Lives in Sydney, Australia • Occupation Data Scientist

From as early as I can remember, I was always interested in maths and numbers. My grandad used to tell the story of me as a young child adding up the numbers on the back of buses on the way to pick up my sister from school. At school, maths was my favourite subject and something that I found came easily to me. When I finished high school, I really didn’t have any clear idea of what I wanted to do as a career, which made picking a university degree difficult. I wanted to do something where I felt like I was positively contributing to society, and a job in a medical field seemed like an obvious choice. For a while I considered medicinal chemistry, but being in a lab was never very appealing to me. In the end I decided to study something I knew I enjoyed, and so I enrolled in an advanced mathematics degree. My parents were quite confused why I didn’t choose a degree with a defined profession such as medicine or law, and questioned me about what kind of career I could have after studying mathematics. I didn’t have a good answer for that, but felt confident that if I did something I enjoyed, the career aspect of things would work itself out later.

(…) My grandmother was suffering from Alzheimer’s disease, so the possibility of making a contribution in that area by studying the brain was very appealing.

In my second year of undergraduate study I discovered the subject of biomathematics, which involves using quantitative methods to study the biological world. I found it really interesting, and ended up doing my honours project in the field, modelling molecular diffusion in cells. When I came to the end of my degree, however, there still wasn’t an obvious career path for mathematics graduates. Careers days were dominated by financial institutions, and I ended up accepting a position as a quantitative analyst at a large investment bank. It only took me a few months to realise this wasn’t the right path for me, and I started looking for other opportunities. I’d enjoyed the research aspect of my honours year, and so thought a PhD in a field like biomathematics could be a good option. There wasn’t much research happening in Australia in this area, but I read a lot coming out of UK universities such as Oxford and Cambridge. Coming from Australia, I’d never imagined that I would be able to get into such prestigious universities, but decided there was no harm in applying. At that time, my grandmother was suffering from Alzheimer’s disease, so the possibility of making a contribution in that area by studying the brain was very appealing. I managed to find a supervisor at Cambridge University working in the field of computational neuroscience, and was lucky enough to be accepted into a Wellcome Trust programme that would fund my PhD in that area.

I greatly enjoyed my time studying in Cambridge, and met a lot of interesting people. One thing I noticed was that although there were many talented female PhD students in the mathematics department, I met almost no female postdoctoral researchers. I believe the impermancy of contracts and often frequent relocation involved in the early stages of an academic career are aspects which turn women off pursuing academics, particularly those who want a family. These were certainly factors that influenced my decision not to continue in academia, and at the end of my PhD I instead looked for opportunities in industry back in Australia.

(…) Choosing to study mathematics has given me fundamental skills in logical reasoning and problem solving which can be applied across many industries and careers.

I spent a year working as a data scientist at a neurotechnology startup in Sydney, but found that the company’s small size meant that it was difficult to produce any meaningful insights with the limited amount of data available. I also realised that I was more interested in working on challenging and meaningful problems from a mathematical perspective, rather than their precise applications. These factors lead me to take a position outside neuroscience, at an aerial imagery company called Nearmap. I’ve been working there for over two years now, helping build models and systems for automatically detecting objects in aerial imagery. I’ve greatly enjoyed my time there, and have been lucky enough to work with a number of talented women within the artificial intelligence team.

If there’s any advice I would give young people choosing what to study, it would be to do what you enjoy and are passionate about, and don’t worry too much about a degree’s application to a career path. My job today isn’t something I would have imagined doing while at university, at which time the field of machine learning as it is today barely even existed. Technology advances so rapidly that it’s impossible to predict what the most exciting and important careers might be in the future. However, choosing to study mathematics has given me fundamental skills in logical reasoning and problem solving which can be applied across many industries and careers.

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