Business Data Scientist
Salomonsen Juul
Business Data Science student with a passion for data, statistics, and software engineering. A curious, goal-oriented individual building at the intersection of analytical rigour and practical systems.
"I am 25 years old and finishing a master's in Business Data Science. Curious and focused when encountering new knowledge, methods, or theories. Beyond programming, I bring a background in business management and operations — and having worked in groups, I understand how to collaborate and make teams work."
— Mathias Salomonsen Juul
End-to-end luxury e-commerce portfolio — FastAPI backend, SQLite database, synthetic data pipeline, KMeans/RF/GBM ML models, and a static BI dashboard on GitHub Pages.
Automated weekly pipeline fetching sustainability news, summarising with Gemini 2.5, and generating strategic business consultations — orchestrated by GitHub Actions.
RAG chat application for internal documents — OpenAI embeddings in ChromaDB, GPT-4o grounded generation, and a FastAPI backend.
Communication network from the Enron dataset — PageRank, betweenness centrality, community detection, LDA topic modelling, and an interactive PyVis graph.
Granger causality analysis of Danish renewable energy vs. electricity spot prices — VAR modelling, impulse response functions, and a Dockerised FastAPI results service.
Transforms academic PDFs into two-host podcast audio — Llama 3.1 for script generation, Suno Bark TTS for synthesis, plus a knowledge graph and quiz generator.
Two-stage Gemini pipeline classifying customer region and emotional type, retrieving behavioural context, and generating a personalised outreach message in a tradesperson's voice.
Master's thesis. Synthetic Danish PSOAP clinical note generation using GPT-4o, evaluated through a downstream patient diagnosis pipeline within a Design Science Research framework.
Primary language across all projects — from data pipelines and statistical modelling to REST APIs and LLM orchestration. Comfortable with the full ecosystem: pandas, NumPy, FastAPI, scikit-learn, and more.
End-to-end data work: ingestion, cleaning, transformation, and exploratory analysis. Experience with structured tabular data, time series, text corpora, and graph data — always focused on reliable, reproducible outputs.
Applied statistical reasoning underpins the work — hypothesis testing, stationarity analysis, Granger causality, VAR modelling, regression, and probabilistic modelling. Equally comfortable with theory and implementation.
Rapid, end-to-end system building — from notebook proof-of-concept to deployed application. Includes API design, containerisation with Docker, GitHub Actions automation, and lightweight frontends.
Relational database design and querying for analytical and transactional use cases. Experience with SQLite and SQLAlchemy ORM — structuring schemas, seeding data, and integrating databases into backend services.
Hands-on with the modern LLM stack — OpenAI, Gemini, and open-source models via Hugging Face. Covers prompt engineering, RAG pipelines, function calling, vector databases, and evaluation workflows across multiple projects.
I am a Business Data Science student at Aalborg University, working toward a cand. merc. at the intersection of analytical methods, software engineering, and domain knowledge.
My work spans the full stack — statistical modelling, machine learning, LLM pipelines, backend APIs, and production deployments. I am drawn to projects where rigorous methodology and practical systems thinking come together to produce something that genuinely works.
I approach problems with a goal-oriented mindset and a preference for building end-to-end rather than in isolation — equally at home in a Jupyter notebook, a FastAPI service, or a CI/CD pipeline.
My thesis in clinical NLP reflects a deeper interest in applying language models responsibly in high-stakes domains — combining solid methodology with practical system design.
Cand. merc. Business Data Science
Aalborg University
Master's programme combining business strategy, statistics, and software engineering. Thesis in clinical NLP: synthetic Danish PSOAP note generation and evaluation within a Design Science Research framework.
HA — Business Administration
Aalborg University
Foundation in business economics, organisational theory, and quantitative methods — the domain context that grounds the data science work.
Higher Commercial Examination (HHX)
Business College
Upper secondary education with a business and economics focus — providing the commercial foundation that underpins subsequent academic and professional development.