I am a senior quantitative risk analyst and soon-to-be PhD graduate in marketing analytics. Welcome to my personal website, where I share ideas and projects that go beyond my academic papers (including some that are still in progress). Think of it as a place to discover my professional journey and the approaches, tools, and frameworks I’ve built along the way. If something here sparks your curiosity or you’d like to connect, feel free to reach out!
Contact
If there’s one thing I’ve learned over the years, it’s that some stories are easier to show than tell. That is why I made this Venn diagram. It sums up how different parts of my journey have come together.
Before starting my PhD, I worked on projects with government agencies, where I learned how data, policy, and decision-making intersect. That experience taught me the importance of impact communication, how to translate numbers into stories that actually drive action.
Then came my PhD journey, which took me deep into retail pricing, omnichannel behavior, and platform engagement. It sharpened how I think about context, behavior, and timing, and it taught me to turn complex findings into frameworks that make sense. I also got better at insight translation: making technical results digestible for non-technical audiences. Whether it’s a dashboard, a short briefing, or an executive summary.
Now, at my current role, everything comes together. I develop monitoring frameworks and keep models in check and make sure our analytics stay reliable and scalable. It’s where I bring together analytical rigor, research thinking, and clear communication to ensure the models we trust actually drive better decisions.
About MeI am (by training) an economist, a quantitative risk analyst, a strategist, a policy maker, a researcher and a marketer who can code/program (in R, Python, SAS and Spark) and run advance statistical analyses using data analytics techniques. In my current role, I am responsible for (credit risk) model monitoring and developing a testing framework, specifically for Loss Given Default (LGD: IRB & IFRS-9), Probability of Default (PD: IRB), Expected Credit Loss (ECL: IFRS9) and concentration risk models.
Linkedin Download My CVHow Does Long-Term Adoption of Online Grocery Channels Affect Customers' Purchasing Behaviors? Insights from Nordic Grocery Data.
Find out (Slides) Code WalkthroughWhat Influences the Power of a Discount in Retail? Unveiling Hidden Factors with Nordic Grocery Data Across Hypermarkets, Supermarkets, and Convenience Stores.
Find out (Slides) Code WalkthroughHow Do Regular Prices and Discounts Uniquely Influence Retail Sales? Exploring Asymmetric Effects Across Store Formats with Insightful Nordic Grocery Data.
Find out (Slides) Code WalkthroughWho Gained the Most Playlist Followers During the Pandemic? Exploring the Surge in Spotify Playlist Popularity Amidst COVID-19.
Find out (Slides) Code Walkthrough