Johannes Janousek

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

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Experience

Penxion Technical Founder | Jan 2024 - Dec 2025 | Berlin

• Built a platform helping seniors and their caretakers with care related tasks in Germany.
• Developed Al-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 Al 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) Data Scientist | Aug 2022 - Jan 2024 | 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 Machine Learning Engineer | Dec 2019 - Jul 2022 | 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 Quantitative Analyst | Jan 2016 - Sep 2017 | 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.

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Projects

Task Adaptive LoRAs Instant Transformer Adaptation with DSPy | github.com/jjanousek/dspy-t21

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

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Education

King's College London MS Data Science and Statistics | Sep 2018 - Sep 2019 | 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 Data Science Course | Oct 2017 - Feb 2018

Regent's University London BS Finance | Feb 2013 - Jan 2016 | Distinction

• Dissertation: Valuation Model for Young Tech Companies.

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Other

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