Curriculum Vitae
Dr. Jeremy Rigney
Senior Data Scientist · AI R&D · Astrophysicist
A PhD physicist turned data scientist — building large-scale data pipelines and models, with a decade of Python and a research record spanning international, peer-reviewed astrophysics.
01 — Education
Where it was built.
Doctorate of Philosophy — Astrophysics
2020 — 2024Queen's University Belfast · School of Mathematics and Physics
- 2020 Eric Lindsay Scholarship, jointly hosted by Dublin Institute for Advanced Studies and Armagh Observatory and Planetarium.
- Designed and operated high-throughput data pipelines processing >100TB of time-series data from multi-instrument sensor arrays.
- Managed real-time data acquisition as Chief Observer, overseeing streams of up to 3 GB/s with a focus on data integrity, storage, and downstream signal processing.
- Engineered feature extraction and anomaly detection systems to identify rare signals in high-noise environments.
- Implemented parallelised, containerised workflows (Slurm, Singularity) to ensure repeatable, audited data processing in high-compute environments.
- Validated complex models by comparing simulated datasets against empirical measurements.
- Published peer-reviewed research in leading international journals (Nature Astronomy and Astronomy & Astrophysics).
- Award-winning science communicator, recognised for translating highly technical astrophysical and mathematical concepts for diverse, non-specialist audiences.
Bachelor of Science (Honours) — Physics with Astronomy & Space Science
2016 — 2020University College Dublin
- Relevant coursework: Probability and Statistics, Databases and Information Systems (SQL), Applied Mathematics, Linear Algebra, Calculus.
02 — Experience
A few places my work lives.
Senior Data Scientist
Mar 2026 — PresentOptum
Data Scientist
Jul 2025 — Mar 2026LexisNexis Risk Solutions
PhD Researcher
Sep 2020 — Dec 2024Queen's University Belfast
- Designed and operated high-throughput data pipelines processing >100TB of time-series data from multi-instrument sensor arrays.
- Managed real-time data acquisition as Chief Observer, overseeing streams of up to 3 GB/s with a focus on data integrity, storage, and downstream signal processing.
- Engineered feature extraction and anomaly detection systems to identify rare signals in high-noise environments.
- Implemented parallelised, containerised workflows (Slurm, Singularity) to ensure repeatable, audited data processing in high-compute environments.
- Validated complex models by comparing simulated datasets against empirical measurements.
- Award-winning science communicator, recognised for translating highly technical astrophysical and mathematical concepts for diverse, non-specialist audiences.
03 — Technical Skills
The toolkit.
AI & Machine Learning
Large Language Models (LLMs), applied ML for anomaly detection and signal classification.
Programming
Python (8+ years), SQL (Advanced), Bash, R.
Data Engineering
Azure, AWS, high-volume ETL, Parquet, batch & stream processing.
Specialised Analysis
Time-series signal processing, sensor fusion, multi-channel data analysis.
Data Privacy & Security
Data anonymisation / de-identification, GDPR-compliant data design.
Software Practices
Production-grade Python (PEP8, type hinting, docstrings), modular design, unit testing (PyTest), CI/CD, Docker, Git-flow.
04 — Awards
Recognition.
- 2024Institute of Physics Three-Minute Wonder UK & Ireland Audience Award (Royal Institution, London, United Kingdom).
- 2024IOP Rosse Medal for Best Postgraduate Presentation (IOP Irish HQ, Dublin, Ireland).
- 2022Peter Curran Award for Best Postgraduate Presentation (Irish National Astronomy Meeting, Dublin, Ireland).
05 — Publications
Peer-reviewed research.
- Multiwavelength observations of flaring activity on the Sun and M dwarf stars
- Radio signatures of star–planet interactions, exoplanets and space weather
- Tracking the motion of a shock along a channel in the low solar corona
- Searching for stellar flares from low-mass stars using ASKAP and TESS
- First results from the REAL-time Transient Acquisition backend (REALTA) at the Irish LOFAR station
- Comparing Game Based Learning, using a student created game, to Traditional Classroom Methods