Tim Lenzen
I am currently working as a Senior Data Scientist at Ailio. My focus is on helping improve organizations by better utilizing their data. I contribute to these transformation projects by bringing in my broad expertise in data related topics ranging from data engineering and cloud-development (AWS, Azure) over data science and machine learning to communication and leadership skills.
After completing my masters in chemistry, I really started my journey in the data science and machine learning field during my PhD studies in theoretical chemistry. The next step for me was a role as a data scientist in a company developing software in the IT-Security field. For five years, I worked on a system to detect suspicious e-mail traffic using machine learning. Set aside the technical aspect of the job, I also built a small team. From this experience I learnt a lot about leadership and developing software products on a larger scale.
I strongly believe that using the right data to inform important decisions helps organizations of all kinds improve. However, often this is easier said than done. I am always curios to discover and tackle these interesting challenges. Also, I am more than happy to sharing my knowledge and learnings.
Session
In the data science field, we use all these powerful methods to solve important problems. Most of the time, we do this very well because our data science and machine-learning toolbox fits the problems we tackle quite precisely. Yet, what about our everyday choices or even our most important life decisions? Can we use for our private lives what we advocate for in our jobs or are these choices inherently different?
Many of this real life decisions are a little different than textbook machine-learning problems. There is often less or hard-to-come-by data and the decisions are infrequent, but sometimes very consequential. This talk will dive into what makes everyday decisions difficult to handle with our data science toolbox. It will show how Bayesian thinking can help to reason in such cases, especially when there is not a lot of data to rely on.