People are experiencing various passages of lives and have finally shaped what they are today, so did us, each one of us at Jerryrat. This year, we will be down to explore the application of marketing science in the banking industry, where there is a large pool of diverse client individuals. Therefore, it is crucially important to have capable cooperators, like us, who share common goals to reach further in the product perfectness while bring in diverse perspectives and viewpoints from our unique mindsets and varying backgrounds. In this README document we will discuss our strength in terms of diversity. We will also state where our team lacks knowledge backgroud and diversity and the impacts. We will expand on how to tackle the problems by subject matter experts, and how people from distinct diverse groups may contribute our team.
Sharing the same major in Computer Science, we are pursuing similar but also different trajectories. Angelo and John, as stduents majoring in Mathematics at the same time, they provide valuable numerical insights into the mathematical optimization which is a bonus point when it comes to addressing the model efficiency. While we also experienced different industry paths. John has empirical practice at a startup as a software developer. He was responsible for speech-to-word and semantic recognition part for an intra-company platform's search engine. The NLP expertise is transferrable to the modeling of campaigining data optimization. For Angelo, he has multiple internship experience at large tech corporates, and could help formalize the general app architecture and establish professional industry workflow. Besides, his background dev expertise boosts up the dev cycle flow and will largely accelerate the development progress.
Junming majors in Statistics and also has experience in startups working on computer vision projects. He has rich experience in research as well and covers wider grounds these years to continuously expands his career views and enhance his academic rigidity. He worked on data warehouse management, and was assigned with a database operation demo in Java Spring framework to learn. He learned the streaming system (Flink), and worked on a model (Yolov5) to detect if someone wears a hat and deployed a data pipeline (Kafka) to transmit the result. He also contributed a lot to database maintenance and will also be helpful for the storage management and data pipeline engineering in our app dev.
Junhong and Hunter are specializing in Data Science apart from Computer Science. Hunter did a research intern at Huawei Noah Ark's Lab and is consolidating deeper senses in the deriving sub-industries, like computational marketing etc. Along with Hunter, Junhong is exercising an in-depth explorance in the campaigining strategies and the client-side markets due to her rich experience in the customer relation management.
Although we have complementary skillsets to launch a project in a startup setting, it is challenging to effectively operate a business entity given that it is under North American markets. We are all coming from an international backgrounds and don't have too much insight in running a tech startup in Canada. Besides, we will need to gain more domain knowledge in marketing science because it is a non-intersected field with our majors, and it is important to grind on its methodologies and thought process to be more familiar with its application in the real world. Moreover, soft skills are also one thing to be mentioned. We are fresh pre-grad techies and not sophisticated enough to handle business logistics, while the realm of unicorns have more to demonstrate other than those exceptional works, and some valuable qualities are another half of the plate, like good grasp of personnel, management methodologies, decision making, etc.
Except for the lack of diversity in hard and soft skills, we realize the lack of diversity in the latent but crucial domains. One example is that we do not have enough prior knowledge about customer multiformity in Canada. Canada consists of immigrants from everywhere in the world, like East Asia, India, Middle East, Africa, Europe, etc. Immigrants have different cultural, race, economic and ideological backgrounds and different personal conditions such as sexual orientation, age, health, and religion. If we do not consider this during designing our products, such as algorithm fairness, the final effectiveness of our product might be weakened, such as distorted analysis results made by our products. We have heard severe problems in financial services caused by unintentional ignorance of diversity. For example, one unconscious racial bias in Canadian banks is that "a base level of heightened scrutiny towards a person of color" based on Fo Neimi, a co-founder of CRARR [1]. Therefore, during our strategy-making and product development, later on, we need to carefully weigh diversities of customers, not only limited to race consideration given example above.
To solve these issues, we will arrange to meet up with professionals from the banks and those working in the marketing/public advertising industries, or credit card specialties. They are the field experts in either marketing science or credit cards, and will be more familiar with the Canadian market. Apart from these, we will also talk to more entrpreneurers to consult about how to figure out the problems, the approcahes to them and how to effectively run a business from scratch. We will consult researchers and experienced engineers for technical advice during developping our product.
Our financial status has limited us from broadening the views onto those within totally different economic stance. There are a considerable portion of population earning below-averaged annual salaries, and they are not supposed to represent those majority who have normal affordability. If we are drafting campaigning strategies that embrace higher level of diversity, it entails to cover and consider the images among multiple economic classes. Jerryrat is therefore managing to diversifying our customer base by ensuring that most socio-economic demographics are included.
The lack of enough representation of one social class may lead to under-performance of the campaigning process and biasedness in the optimization model. We are focusing on the credit card product and the card benefits are major concerns while evaluating the data obtained from some biased process. Our app also analyses the data and re-adjusts weights related to products, offers, and variables related to the whole process, in order to reach an optimized objective function for further campaign. If the bias remains, it will be detrimental to campanies that use the app and affect the decision-making routines. Therefore, we will do our best to make our product open and inclusive to customers from distinct backgrounds, such as adopting analysis models adjusted to adapt fairness.
[1] Koho. (n.d.). Unconscious bias at the bank: Racial profiling of BIPOC Canadians while banking. Unconscious Bias At The Bank | Racial Profiling Of BIPOC Canadians While Banking. Retrieved September 28, 2021, from https://www.koho.ca/learn/unconscious-bias-at-the-bank/.