Starting your career journey in data science: Q&A with Jaywing's industrial placement students

Question: Tell us about your role at Jaywing?

Abisola: “At Jaywing, my role as an analyst is dynamic and ever evolving. Through this journey, I have been introduced to the world of credit risk, fraud analysis, and even aspects of IFRS9, an accounting standard for lenders, navigating through diverse datasets with tools like BigQuery, SQL, Snowflake, and Excel. What truly enriches my experience is the opportunity to collaborate and learn from seasoned professionals. Working alongside talented individuals who bring years of expertise and knowledge has been invaluable in shaping my understanding and skill.

“My role at Jaywing not only involves handling data and building models but also encompasses a continuous journey of learning and growth, facilitated by a supportive environment and mentorship from experienced professionals.”

Lily: “As a placement student at Jaywing, I have been rotating between the different teams involved within the larger Data Science team. So far, I have spent most of my time working within the UX/CRO team in which I have created and presented decks which include heuristic reviews, GA data and attention mapping. I have particularly enjoyed the presenting side.

“I am now moving to the Data Engineering team, where I will be more involved with looking at data and comparing and analysing it.”

Question: What led you to study Data Science and what has your career journey looked like until now?

Abisola: “My journey stemmed from an interest in problem-solving and a recognition of technology’s potential. This passion was sparked during my undergraduate studies in Management Information Systems, where I was introduced to coding and analysis. I remember the first time I saw what I could do with Java, and I was hooked. Building on this foundation, I transitioned into a role as a data analyst at a utility company, where I used tools such as SQL and Excel to extract information for both business decisions and regulatory compliance.

“Driven by a desire to expand my skill set and widen my expertise, I pursued a master’s programme in Big Data Analysis, where I explored diverse tools including R, Python and Hadoop, while also deepening my understanding of various statistical methodologies. These experiences not only honed my technical skills but also reinforced my passion for using data to solve problems and drive meaningful decisions. This led me to my placement at Jaywing, where I’ve had the opportunity to further harness my skills and expertise. It has introduced me to a field where I can effectively apply my knowledge and I am eager to continue growing and making valuable contributions as part of an exceptional team during the rest of my placement and beyond.”

Lily: “I have always had a logical and mathematical mind, and studying Economics at University has furthered this. Getting into data science felt inevitable with how my mind has always worked and where I’ve always been passionate. To combine finding solutions with mathematics makes a lot of sense to me. My placement year has been the first time I have been involved within a Data Science team and been able to apply all that I’ve learnt to my career. I’m looking forward to continuing this.”

Question: Tell me about any projects that you have worked on at Jaywing that you have particularly enjoyed?

Abisola: “I have been involved in an interesting analysis project, conducted under the guidance of Nick Sime who is the Head of Modelling at Jaywing. In collaboration with another placement student, Mustafa Khoshnaw, we aimed to enhance the efficiency and effectiveness of a set of fraud policy rules. These rules, which form the digital identity of applicants, encompass factors like email age, transaction value, device type, and black-listed profiles. They are assigned weights based on perceived risk and influence the system's decision-making process on application acceptance, referral, or rejection.

“What made this project particularly enjoyable was its comprehensive nature. We performed reject inference to deduce the behaviour of applications automatically declined by the system and compared actual and expected fraud rates to identify statistically significant differences. Through comprehensive analyses, we explored multiple approaches to find a solution that optimises performance while minimising disruption to the existing system. The option chosen would lead to reduced fraud, thereby reducing loss and enhancing financial benefits to the client.

“Collaboration was essential throughout the project, from validating our chosen option using out-of-time data to preparing presentation slides. This project alongside others I have been able to contribute to, has provided invaluable hands-on experience. I have also been able to see the practical application of data science in real-world scenarios.”

Lily: “Within the UX/CRO team I have worked on a multitude of projects. One client that particularly stands out, is working with a high street retail lender. The email design review was both the first piece of work I did by myself and my first presentation. This helped the team form a strong relationship with the client and I have subsequently helped on other projects for them. I also loved the Landmark Trust’s Visual Refresh project. It was my first time working with Userlytics, which includes video responses to questions as opposed to written answers. Watching, clipping then eventually presenting the videos was a fun process and I loved being part of the whole project.

“My journey in the Data Engineering team is just beginning but I know I will be working on a large banking client which I am so excited to be involved in. Having been shown what goes on with the processing and organisation of the data, I have been completely inspired by how their minds work. I look forward to trying this myself.”

Question: Do you have any role models in data science? If so, who are they and why do you find them inspiring?

Abisola: “I find inspiration in the achievements and contributions of several role models in the field, particularly female leaders who have paved the way for others like me. One such figure is Dr. Fei-Fei Li, whose work in machine learning, deep learning and computer vision has not only advanced AI research but has also emphasised the importance of diversity and inclusivity in technology.

“Similarly, I have long admired Kimberly Bryant, the founder of Black Girls CODE, for her dedication to empowering young girls to pursue careers in technology. Through the organisation she founded, she created a platform that encourages young girls to pursue their passions and break down barriers in the industry.

“Also, my time here has allowed me to meet and work with individuals like Katie Stones and Nick Sime, whose guidance, extensive knowledge, and support have been instrumental in my growth and learning journey, enriching my experience here.

Lily: “My role model in data science would have to be Kira Radinsky and the way she uses data science for real world predictions. Kira successfully predicted the cholera outbreak in Cuba in 2012 and her later work continued with the COVID-19 outbreak, in which her company created an AI powered self-check home triage medical platform which predicts where, when and how fast COVID will spread with 73% accuracy.”

Question: What advice would you give to other women thinking about starting a career in data science?

Abisola: “As someone who is navigating the world of data science myself, my advice to fellow women considering this field is simple: embrace your passion and just dive in. Believe in yourself and do not shy away from challenges, they can serve as a way to grow.

“Looking back, I wish I had found mentors sooner. Having people who have been there and have done what you want to do can be a game-changer. They can help navigate the way for you, give insights, guidance, and they can also be sympathetic ears when needed.

“Data science is a rapidly evolving field, so keep learning. Commit to continuous skill development through a formal education, online courses, or hands-on projects, just keep pushing yourself forward.

“Above all, enjoy the journey and celebrate every step along the way.”

Lily: “Because you are a woman, makes no difference to how the career should be approached. From my point of view, appreciate the opportunity and enjoy a career in data science because you love it.

“Although statistically, women are less dominant in this field of work, the good work you do will always be praised and appreciated because of your skills as a data scientist regardless of your gender. So, in a nutshell, my advice would be to make a difference by solving problems with data because that is what you love to do!”

To read more about inspiring inclusion and the career journeys of the women in data science at Jaywing, click here.