15 years of data and media experience at RTL. Today I combine weather data, Python and AI into mobile applications for iOS and Android.
Behind the scenes at RTL I automated weather graphics systems and visualised complex weather data – first in Matlab and AutoIt, later in Python and JavaScript. As a meteorologist, alongside the core work such as presenter briefings and severe weather warnings, I prepared weather information for a general audience in formats like the n-tv weather week.
Today I combine weather data, databases and AI in my own apps. To build small, purpose-made models for them myself, I'm going deep into machine learning and recently completed the Machine Learning Specialization from DeepLearning.AI.
I want to bring this mix of domain expertise, data engineering and product development to a team building data-driven AI solutions – and train further there in data science and AI development.
A full-time role and further training in data science or AI development, including outside meteorology.
Designing and building my own Flutter apps for iOS and Android: weather APIs, Supabase backend, AI image recognition with Gemini. CamperCompass is live in the app stores.
Automated the weather graphics systems; processed and visualised weather data in Matlab, AutoIt, Python and JavaScript. Briefed presenters, issued severe weather warnings and wrote weather articles for ntv.de and wetter.de, including the weekly weather round-up.
Atmospheric physics, statistics and scientific data analysis.
Wind warning and sun compass for campers. Personalised gust limit for the awning, MeteoAlarm severe weather alerts and an 8-day forecast.
Fishing app with AI fish recognition from photos, AI tactic advice based on weather, and wave height calculation using the SMB method.
Shows before you ride where tailwind, crosswind or headwind awaits along your route. Real road-network routing, route import (GPX, KML, GeoJSON, CSV) and time-shifted wind forecasts per section – plus the best time window to set off.
Independent work samples where I handled data sourcing, feature engineering and modelling myself, end to end.
Daily bike counts from Cologne's Stadtwald counting station, combined with DWD weather data and calendar information (public holidays, school breaks, FC Köln home games) and modelled with linear regression — including feature engineering, multicollinearity checks and error analysis.
Open to full-time roles in data science and AI development, including outside meteorology. I look forward to hearing from you.