A brief introduction:
Hello Everyone! My name is Dominik Brändle.
Both professionally and personally, I am deeply engaged in the fields of data engineering and financial analysis. In today's era, numerous opportunities arise to optimize processes through data-driven approaches and enhance efficiency. It's this very perspective that drives me each day, motivating me to always give my best. My professional career started in mechanical engineering and plant construction. Since 2019, I have been working in the financial industry at Kaiser Partner Privatbank in Vaduz, Liechtenstein. My roles in risk management (market and operational risk), project management, and data engineering have allowed me to acquire a multitude of experiences and skills.
In my current role as a Data Warehouse Engineer, the subjects of Big Data, Artificial Intelligence (AI), and Data Science play a significant role – and will continue to gain even more importance in the future, particularly in terms of data-driven decision-making. Throughout my studies, I delved deeply into the topic of Empirical-Asset-Pricing. Utilizing the programming language R, I replicated several scientific papers, including works such as Fama and French (2010), Frazzini and Pedersen (2014), Novy-Marx (2012) and more. The greatest challenges often emerged from data preprocessing. As a result, I acquired comprehensive knowledge in areas such as data cleaning, data wrangling, data processing, and time series modeling – all which tie into my current role. In terms of research methods in finance research papers, there has been an increasing adoption of Machine-Learning (ML) methods, sparking my interest in the field of AI.
Why did I take up the FDP challenge?
In my role as a Data Warehouse Engineer, I consistently deal with large datasets. Currently, AI-based systems are opening up additional business opportunities, implying both opportunities and risks. Therefore, it is of particular importance for me to acquire a solid understanding of AI to make informed decisions regarding benefits and costs. Based on the FDP curriculum, I had the expectation to gain a comprehensive understanding of AI in the finance and banking industry and to gain insights into best practices.
What is great about FDP:
In my previous engagement with ML and Data Science literature, I often approached it in a rather specific manner. Essentially, I lacked a comprehensive overview. This is exactly what the FDP curriculum provides – it covers all relevant areas in ML and Data Science in Finance. And it does so with the right balance between breadth and depth, supported by meaningful and relevant literature.
The study and preparation for the FDP exam, for instance, has helped me to differentiate between significant ML use cases and noisy ones – since the curriculum gives you a strong sense of state-of-the-art solutions. Personally, this advantage enables me to identify operational optimization potential or new strategic business opportunities. During the exam preparation phase, the FDP Institute is available for questions and clarifications at any time, providing excellent support. Additionally, what I truly value are the engaging webinars featuring distinguished personalities from the finance industry, which provide inspiring insights into the applications of ML in finance.
My advice for aspiring industry professionals:
It is important to realize that in the financial industry, primarily everything revolves around the processing of information. Whether it's analyzing stock market data, conducting client consultations, carrying out creditworthiness assessments, transmitting regulatory reports, or disclosing financial figures, it all involves a substantial potential to make processes more efficient, especially through the automation of repetitive tasks. With a fundamental understanding of AI and Data Science, you certainly have a solid foundation for a successful career in the finance sector. In practice, the key is to then integrate these skills with business logic to generate added value. There's a saying that nothing is more constant than change. Therefore, it's crucial to stay continuously engaged - engage in lifelong learning, question existing beliefs, and explore new avenues.
About Dominik Brändle
Dominik is a Data Warehouse Engineer at Kaiser Partner Privatbank in Vaduz, Liechtenstein. He holds an MSc. in Finance from the University of Liechtenstein and obtained the Financial Data Professional (FDP) Charter. His areas of expertise encompass data engineering, portfolio backtesting, and quantitative risk analysis.
Dominik Brändle, FDP
Data Warehouse Engineer
Kaiser Partner Privatbank
Dominik Brändle - Translator
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