About
I am a Senior Data Scientist with over 6 years of experience across AI systems, statistical modelling, and large-scale scientific computing. I've spent my career delivering production-ready ML systems in sectors ranging from healthcare and fintech to renewable energy. I’m a Senior Data Scientist with over six years of experience building AI systems and statistical models for production. My career has been spent delivering production-ready ML systems into the real world, with projects spanning healthcare, fintech, and renewable energy.
Currently, I'm working as a Freelance Data Scientist, specializing in agentic AI architectures and anomaly detection. I'm passionate about building intelligent systems that don't just work in a lab, but drive measurable, real-world impact.
My background includes a Ph.D. in Particle Physics and time spent with the ATLAS experiment at CERN, which gave me a fairly rigorous lens for looking at data. Whether I'm working on energy simulations or financial platforms, I enjoy the challenge of turning messy datasets into something actually useful.
My Approach
I bridge the gap between the high-energy physics academic rigor and agile AI deployment. My philosophy is simple: "Data Integrity First." If the foundation isn't solid, there’s no point in building a complex model on top of it. I specialize in the "last mile"—taking research-grade concepts and turning them into robust systems that can actually scale. Outside of work, I’m a big believer in staying balanced. I spend my time hanging out with my family, experimenting with 3D printing, or playing some light competitive sports. These hobbies keep me creative and help me stay sharp when I get back to my professional projects.
Outside of the technical world, I am a firm believer in a balanced life. Whether I'm engaging in competitive sports, experimenting with 3D printing, or enjoying quality time with my family, these personal pursuits fuel the creativity and resilience I bring to my professional work.
Impact by the Numbers
My work is driven by measurable results—from accelerating physics simulations to improving healthcare outcomes.
Latency Reduction (x)
Model Accuracy (%)
Peer Citations
Years in AI & Data
Skills
OSs
Coding
AI & Machine Learning
Cloud & Infrastructure
Data & Analytics
Languages
Resume
Download CVEducation
PhD in Particle Physics
Sept 2017 - Jul 2021
University of Sussex, Brighton, UK
"Search for supersymmetry with the ATLAS detector at the Large Hadron Collider in final states with two hadronically decaying τ-leptons."
MPhys in Physics and Astrophysics
2013 - 2017
University of Sussex, Brighton, UK
Grade: 1st class degree with honours.
"Simulation studies for e+e-→VH final states at a linear collider in the standard effective field theory."
International Baccalaureate
2010 - 2014
International School of Geneva, Geneva, Switzerland
Grade: 34
Higher Level subject: Physics, Mathematics, Economics
Standard Level subjects: English, Spanish, Chemistry
Professional Experience
Freelance Data Scientist
Apr 2025 - Present
Transparently.AI, Singapore
At Transparently.AI, I stepped into the fast-paced world of fintech to architect a production-ready agentic AI system from the ground up. My primary focus was bridging the gap between raw, unstructured financial reports and structured analytical outputs. By designing sophisticated RAG-based pipelines and implementing adaptive anomaly detection, I built a system capable of continuous, real-time financial monitoring—providing the "intelligence" and "memory" necessary for complex forensic accounting.
Data Analyst (Part-time)
Sept 2024 – April 2025
Terre Innovative Healthcare, Remote
My work with Terre Innovative was centered on high-impact, mission-driven analytics. I developed AI solutions specifically designed for the unique challenges of rural healthcare settings. Beyond just building models, I focused on the end-user, creating validation frameworks and interactive dashboards that translated complex maternal health data into actionable forecasting. This ensured that clinicians on the ground had 90% accurate predictive insights to inform life-saving interventions.
Research & Analytics Lead
May 2023 - Jun 2024
QA-UK Ltd., London, UK
Leading the analytics for QA-UK's green technology portfolio, I managed the full lifecycle of R&D initiatives. I was responsible for prototyping novel technologies and optimizing complex fluid and thermal simulations. By moving our ML workloads to AWS Batch and High-Performance Computing systems, I was able to cut computational costs by 40%—a result that directly supported our successful negotiations for extended project funding and stakeholder buy-in.
Statistical Production Analyst (Development Lead)
Sept 2022 – Mar 2023
Office for National Statistics, London, UK
As a co-lead for an 8-member team at the ONS, I was tasked with the critical collection and analysis of the UK's international trade data. I spearheaded the move toward modern coding standards, replacing legacy processes with automated, error-free Python pipelines. This modernization didn't just improve data quality; it reduced our production time by over two weeks, allowing us to provide faster, more robust insights for government-wide decision-making.
Postdoctoral Research Fellow
Aug 2021 – Aug 2022
University of Sussex / CERN ATLAS, UK & Switzerland
Following my PhD, I focused on the challenge of extreme-scale data processing. I developed high-performance tracking algorithms that achieved a 15x latency reduction through FPGA hardware acceleration (HLS) and software optimization (OpenCL/OpenMP). This role was a balance of deep statistical modeling and high-performance software engineering, where I also mentored postgraduate researchers on ML optimization and experimental design.
PhD Researcher (Particle Physics)
Sept 2017 – Jul 2021
University of Sussex / CERN ATLAS, UK & Switzerland
My doctoral research at the ATLAS experiment involved performing large-scale data analysis on petabytes of collision data to search for rare particles. I designed statistical models and predictive algorithms that enhanced experimental sensitivity by 70, contributing to the broader understanding of supersymmetry. This work required building automated data analysis pipelines for large-scale distributed computing environments and resulted in contributions to over 100 peer-reviewed publications.
Looking for a more detailed version of my career path?
Download Full CV (PDF)AI & Machine Learning Innovation
Highlighting key initiatives where I've applied advanced AI and scientific computing to solve high-stakes problems.
Agentic Financial Forensics
Challenge: Detecting sophisticated fraud in massive, unstructured financial datasets.
Solution: Architected a production-ready agentic AI system using RAG pipelines and automated anomaly detection.
Impact: Enabled continuous, automated financial monitoring with high precision.
Physics Tracking at Scale
Challenge: Real-time processing of petabytes of experimental data at CERN.
Solution: Developed C++/Python tracking algorithms accelerated on FPGA hardware (HLS) and software (OpenCL/OpenMP).
Impact: Achieved a 15x latency reduction, contributing to 100+ cited publications.
Pregnancy Risk Prediction
Challenge: Identifying maternal health risks in low-resource rural settings.
Solution: Built predictive ML models and validation frameworks integrated with interactive Tableau dashboards.
Impact: Delivered 90% cross-validated accuracy for outcome forecasting.
Renewable Energy Optimization
Challenge: Optimizing fluid and thermal simulations for green energy tech.
Solution: Deployed ML workloads on AWS Batch and HPC infrastructure to simulate complex environments.
Impact: Reduced computational costs by 40% and secured extended project funding.
Synthetic Data Generation
Challenge: Working with sensitive patient health data while maintaining privacy.
Solution: Trained GANs/VAEs (TensorFlow/Keras) to generate statistically accurate synthetic patient data.
Impact: Created a secure framework for research partners to draw meaningful conclusions safely.
Open Source Contributions
I maintain several repositories on GitHub focused on computer vision (RCNN Vescicle identification), star identification algorithms, and time-series prediction.
Conferences, Seminars, and Workshops
Xilinx adaptive compute PhD School
CERN openlab - Programming and environments for parallelism
40th International Conference of High Energy Physics (ICHEP)
European School of High-Energy Physics
ATLAS UK Workshop
Large Hadron Collider Physics (LHCP) Conference
21st IEEE Real Time Conference
Awards
UKRI SWIFT HEP Grant Award
2021 - Post-doctoral Fellowship
DISCnet
2017 - Full Scholarship Grant
Institute of High Energy Physics - Beijing, China
2019 - Visiting Scientist
University of Sussex
2017 - PhD scholarship in collaboration with CERN
European School of High Energy Physics
2019 - Outreach Activity Project Winner
MPS Sussex Plus Careers Award
2015 - For the completion of studies in communication, personal skills, and employment cometencies.
Publications
International peer-reviewed conferences/proceedings
-
M. Grandi for the ATLAS Collaboration.
“The ATLAS Inner Detector Trigger performance in pp collisions at 13 TeV during LHC Run 2”.
Proceedings, 40th International Conference on High Energy Physics (ICHEP 2020): Prague, Checz Republic, 29 Jul, 2020
PhD Thesis
-
Mario Grandi and Prof. Fabrizio Salvatore.
“Search for supersymmetry with the ATLAS detector at the Large Hadron Collider in final states with two hadronically decaying τ-leptons.”.
Presented 04 Aug 2021
http://cds.cern.ch/record/2815398
Articles in peer-reviewed journals
-
The ATLAS Collaboration.
“Search for direct stau production in events with two hadronic τ-leptons in √s=13 TeV pp collisions with the ATLAS detector”.
Physical Review D 101, 032009 – Published 28 February 2020
DOI: 10.1103/physrevd.101.032009
https://journals.aps.org/prd/abstract/10.1103/PhysRevD.101.032009 -
The ATLAS Collaboration.
“The ATLAS inner detector trigger performance in pp collisions at 13 TeV during LHC Run 2”.
European Physics Journal C - Published 08 March 2022
DOI: 10.1140/epjc/s10052-021-09920-0
https://link.springer.com/article/10.1140/epjc/s10052-021-09920-0
Personal Interests
A glimpse into the passions that keep me inspired outside of my professional life.
Contact
Location:
Singapore
Email:
dr.mario.grandi@gmail.com
Call:
+65 8307 1535