Samu Syrjänen

I’m a Data Science Master’s student at the University of Helsinki, and a research assistant at Aalto University with one year of experience. My research assistant role has mainly consisted of Data Science related tasks such as developing ML algorithms, analyzing data, and making automated pipelines for various data processing tasks. My background is mainly in Computer Science, Data Engineering, Machine Learning, and Data Analysis. I have also studied Physics (33 cr) and Mathematics (65 cr), and know a thing or two about sailboats and tanks, and how to lead their crews.
I'm looking for long-term work opportunities to gain experience and develop more specialized skills. Future career interests include working with data architecture, pipelines, analytics, cloud platforms, and machine learning to provide solutions for product development, marketing, and business intelligence problems. Besides the technical roles, I'm also able to work in the more hands-on or business administration positions, where a more tech-heavy background might sometimes be beneficial.
- Strengths
- - Enjoy cleaning and organizing data, tasks, and resources to facilitate productivity
- - Education and work experience revolved around data and software since 2019
- - Practical experience with databases, algorithms, and data pipelines
- - Degree structure has a strong emphasis on machine learning, mathematics, and statistics
- - Practical experience with sailboat engines and electric systems, as well as leading sailboat and tank crews.
- Weaknesses
- - Only 1 year's worth of paid work experience
- - No expert-level talent in any specific niche yet
- - A lack of credible business experience
- - Prefer to focus on a single project at a time
Currently looking for work and would prefer to start after graduating, which is expected in early 2026. I'm especially interested in international employers and I want to relocate from my current home, Helsinki.
Career
Research Assistant
Aalto University
As part of Jaan Praks research group at Aalto University and ESA's Hera space mission, I'm responsible for creating a pipeline to clean, calibrate, and analyze hyperspectral image data, and turn it into final data products. The data is received from the ASPECT Hyperspectral Imager on the Hera/Milani space probe. The mission's objective is to gather data about the Didymos binary asteroid system, which was previously in 2022 shot with a DART spacecraft in hopes to succesfully redirect the Didymos's moon, Dimorphos, and to study it's effects. The larger aim is to research this kind of asteroid redirection as a means for planetary defence.
My work entails plenty of data analysis and cross-national coordination between teams working on this project. After the calibration, my pipeline analyzes the hyperspectral images with a convolutional neural network, which will allow us to gain insights into the mineral composition of the target asteroids Didymos and Dimorphos binary asteroid system. The data will be further analyzed with various methods, and documented in scientific literature. The images and derived information will additionally be used to make a reconstructed 3D model containing all related information.
Related to this position, I attended a PDS4 workshop held in ESAC, Madrid, that gave a deep dive into the Planetary Data System (PDS4) archiving format. PDS4 is the latest standard used in NASA's and ESA's scientific data archives.
Research Assistant
University of Helsinki
As part of this space weathering research project, I created a Convolutional Neural Network enhanced Gaussian Process algorithm for estimating asteroid surface age. The algorithm uses asteroid hyperspectral reflectance spectra to give an age estimate. It stands out as a surprisingly flexible algorithm to predict outcomes even with a sparse training set, as in our case. (More details below)
Job CertificateBachelor's Degree in Computer Science
University of Helsinki
Products







Skills
- Python
- Scrum
- Github
- Data Cleaning and Processing
- Databricks
- [ML] Gaussian Process
- SQL
- GPyTorch
- ML training/Hyperparameter Optimization
- [ML] Local Binary Pattern
- Agile Development Methodologies
- [ML] K-Means Clustering
- HTML & CSS
- Medallion Architecture
- ETL/ELT Pipelines
- Spark
- [ML] Convolutional Neural Networks (CNN)
- PyTorch
- Databases
- Software Architectures
- Automatic Testing
- Data Encryption
- Exploratory Data Analysis (EDA)
- AWS
- [ML] Feature Selection Methods
- [ML] Linear Regression
- [ML] Decision Trees
- [ML] Random Forests
- [ML] Gradient Boosting
- OpenCV (Computer Vision)
- JavaScript/TypeScript
Languages
- English CEFR C1
- Finnish Native
- Japanese Beginner
Contact Me
samu.syrjanen@gmail.com