About
I am an engineering leader who builds and scales the ML/AI and data platforms behind quantitative research, combining hands-on ownership of production infrastructure with a strong grounding in ML.
With a background in computational biology, I have successfully applied data technology and engineering leadership to solve tough problems in the pharma industry across the domains of biotech research, IT, supply chain, and manufacturing.
Experience
Head of Software & ML Engineering (Senior Engineering Manager)
Gubra, Digital, Data, and IT, Hørsholm, Denmark — June 2026 – present
I head Software and ML Engineering at Gubra, where my teams apply machine learning and modern software practices to automate and scale scientific processes. I am department manager for two teams, 14 people in total, owning the software and ML engineering organisation behind Gubra’s drug discovery and scientific automation platforms.
Responsibilities and projects:
- Organised the department into two functionally distinct teams: one ML/AI-focused, one software-development-focused, each running its own delivery process (Scrum and Kanban respectively) with a dedicated Product Owner
- Designed and launched an organisation-wide system for deciding, documenting, and enforcing engineering policies, covering CI/CD, technology choices, incident response, code conventions, and more
- Leading a shift from home-grown software to commercial off-the-shelf solutions for undifferentiated capabilities, reserving developer capacity for integrations and business-differentiating work
- Drafting Gubra’s AI and data strategy, shifting the organisation toward a dedicated platform engineering strategy and team that other engineers and scientists build on
- Navigating organisational change as the company scales
Engineering Manager
Gubra, Scientific Automation, Hørsholm, Denmark — May 2025 – May 2026
I built the Scientific Automation team from scratch, establishing the technical practices and delivery cadences that still underpin both teams I lead today, and grew the team from 3 to 7 engineers.
Responsibilities and projects:
- Applied ML, AI, and good software engineering practices to automate and scale scientific processes including drug discovery, digital histopathology, and mass spectrometry
- Managed risk in production ML by keeping humans in the loop: for our scientific image-analysis pipelines, human reviewers pass/fail the most uncertain predictions and can trigger retraining
Senior Developer
Gubra, Software Solutions in Computational Biology, Hørsholm, Denmark — May 2021 – April 2025
I was responsible for the data and app infrastructure for Gubra’s drug discovery platform, working across the full stack: Postgres for data storage, FastAPI for backend, R Shiny for frontend, GitHub Actions for CI/CD, and Docker Swarm for deployment.
Responsibilities and projects:
- Designed and implemented a data-based design-and-analysis loop for drug development using Bayesian statistics and experimental design
- Applied Large Language Models (LLMs) to peptide-based drug design, an early production use of generative AI on the platform
- Built pipelines for automatic data analysis, ensuring uniform, comparable treatment of results across hundreds of experiments
- Introduced Scrum to the team and served as Scrum Master for years, establishing a sprint cadence and facilitating continuous improvement retrospectives
Data Scientist
Novo Nordisk A/S, Manufacturing Intelligence and Supply Chain, Hillerød, Denmark — September 2017 – April 2021
I helped start Novo’s Manufacturing Intelligence initiative, a push to digitalise manufacturing facilities and apply modern data technology to improve equipment efficiency and ensure product quality.
Responsibilities and projects:
- Involved in hiring 15+ data professionals, personally leading 100+ interviews
- Increased overall equipment efficiency of an assembly line by combining Design of Experiments with Gaussian Process Regression and Bayesian optimisation
- Predictive maintenance on brownfield manufacturing equipment using AWS Greengrass, AWS IoT, and CI/CD with Azure DevOps
- Created dashboards for factory planning using Docker, Tableau, and Streamlit
- Built a regulatory framework for applying machine learning to quality-critical pharma processes, covering GxP, model validation, and model monitoring
Intern
Innovation Centre Denmark, Embassy of Denmark to South Korea, Seoul, South Korea — July 2016 – January 2017
Collected market intelligence, facilitated Korean-Danish technology collaboration, arranged and executed large events, and had various diplomatic tasks.
Skills
- Engineering Leadership — Team leadership, team building, org design, hiring, Agile/Scrum, engineering governance, change management, risk-based decision making, and platform & AI strategy, built through several years of managing engineering teams at Gubra.
- Python — 6+ years doing both scripting and programming, comfortable with several modern libraries for data manipulation and data science.
- R — Extensively used for statistics, web app development with Shiny, and Plumber for APIs.
- Statistics — Strong theoretical understanding of classical statistics and hypothesis testing, plus experience applying multivariate statistics, including ML methods like Gaussian Processes and Bayesian optimisation.
- Machine Learning & AI — Solid theoretical understanding of ML and deep learning methods, and 5+ years of experience applying them to various domains, often with sparse or little data. Experience with AWS SageMaker and the Python data science toolbox: Pandas, SciKit, Numpy, Scipy, Tensorflow, etc.
- Databases & SQL — 3+ years developing and using databases, comfortable with multiple dialects of SQL including MSSQL and PostgreSQL, plus some NoSQL experience.
- Computational Biology — Worked with genome data and NGS reads in several projects, including my thesis, with a particular interest in repeatable and automatic pipelines for sequence data analysis.
- Amazon Web Services — Extensive experience building on AWS, including Greengrass, IoT, and SageMaker, for both cloud and edge deployments.
- Containerisation — Extensively used Docker to deploy web apps and APIs.
- Internet of Things — Experience applying IoT to industrial problems, from streaming sensor data to running machine learning models at the edge.
Education
- M.Sc. Bioinformatics and Systems Biology Engineering, Technical University of Denmark, 2017
- B.Sc. Biotechnology Engineering, Technical University of Denmark, 2015