My_CV.doc - WordPad

Johannes Janousek

johannes.janousek@gmail.com | +43 6645457202 | Vienna
linkedin.com/in/j-janousek | github.com/janousek | johannesjanousek.xyz

Experience

Penxion Jan 2024 - Dec 2025

Technical Founder Berlin

Built a platform helping seniors and their caretakers with care related tasks in Germany.
Developed AI-driven web crawlers to compile Germany's largest dataset on senior homes, utilizing multi-modal LLMs to analyze images and extract features.
Engineered data pipelines using Airflow on AWS to process, clean (using Pandas, NumPy), and store crawled and application data across S3, RDS (MySQL, PostgreSQL), and MongoDB.
Created backend tools (using Python, JS, Flask, FastAPI) enabling AI components to generate custom care home reports.
Implemented functionality to assist seniors in filling out crucial forms for Pflegegrad assistance.
Integrated frontend and backend systems via webhooks connected to CRM and EC2 instances for triggering automated processes.

Massar Capital (HFM US Performance Award 2023) Aug 2022 - Jan 2024

Data Scientist Vienna

First hire in newly opened Vienna office; worked directly with CIO across research, data infrastructure, and trading analytics.
Developed a predictive model optimizing the roll dates for >100 illiquid commodity futures, saving $10 million annually in slippage costs.
Researched and developed models to predict surprises in economic releases.
Engineered systems for acquiring, processing, and validating large datasets from exchanges, vendors, Bloomberg Terminal and SQL databases (MySQL, MSSQL) to feed models and analytics.
Acquired and prepared alternative datasets, including satellite imagery for oil inventory analysis, to support trading strategies.
Built and maintained web crawlers and monitoring systems with alerts for time-sensitive trading strategies.
Designed and implemented an interactive analytics tool (Dash, Plotly) enabling the CIO to analyze seasonality trends and translate research into trades.

WeMine Dec 2019 - Jul 2022

Machine Learning Engineer Hong Kong

Developed and deployed a multi-task deep learning recommendation system (TensorFlow, PyTorch) that incorporated user context to drive engagement.
Engineered scalable, low-latency prediction services on Azure using Docker and Kubernetes (AKS).
Designed and automated end-to-end ML pipelines with Airflow, orchestrating data ingestion, preprocessing, training, and deployment.
Systematized ML experimentation and model versioning using MLflow and CometML.
Built custom Docker images to minimize image size and significantly lower cloud hosting costs.

ECO Capex Jan 2016 - Sep 2017

Quantitative Analyst London

Created a credit risk analysis tool in Python and built predictive models to forecast trade success and revenue for a B2B digital marketplace.
Designed and managed a currency basket based on volatility analysis, presenting key performance indicators and analytical outputs to the C-suite using Tableau.

Projects

Task Adaptive LoRAs

Instant Transformer Adaptation with DSPy | GitHub

Re-implemented SakanaAI's Text-to-LoRA research paper with DSPy, generating task-specific adapters in one forward pass and optimizing prompts for the hyper network.

Education

King's College London Sep 2018 - Sep 2019

MS Data Science and Statistics Distinction

Dissertation: Using convolutional neural networks to classify brain-computer-interface EEG data of epilepsy patients.
Modules: Pattern Recognition, Deep Learning and Neural-Networks, Machine Learning, Artificial Intelligence, Databases, Data Warehousing, Statistics.

General Assembly London Oct 2017 - Feb 2018

Data Science Course

Regent's University London Feb 2013 - Jan 2016

BS Finance Distinction

Dissertation: Valuation Model for Young Tech Companies.

Other

Nationality: Austria
Languages: German, English
Interests: Reading, Hiking, Diving, Skiing, Running

Projects

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