Pierre Canavelli

Providing ML & Data Engineering services to clients ranging from seed-stage startups to listed companies. Projects include: - Building the ML back-end for a gaming assets generation app using Stable Diffusion, NeRF - Architecting a multi-cloud data/ML stack for the NFT marketplace of a listed HK company - Creating the MVP of a singing voice generation app using Llama, DiffSinger, HuBert - Auditing 20+ XR/crypto companies in view of integration into an immersive art platform

I founded Bedrock as CTO to build a web3-as-a-service API for the gaming industry. We were incubated with Entrepreneur First at Station F, and raised a € 150,000 pre-seed round. Unfortunately, my cofounder fell out of love with web3. Crypto winters do that to the best of us. If you're a builder with AI and web3 on your mind, shoot me a message see what we can do. - Designed a web3-as-a-service API for the gaming industry - Implemented custom ERC721, 20 and 1155 contracts in Solidity - Built a Python backend to interact with contracts using FastAPI - Built data and API stack using AWS, Docker, Terraform, GitLab - Led alpha test with 12 gaming studios and tech companies - Raised € 150,000 from Entrepreneur First and Bpifrance

Building and killing my first company.

- Hired, trained, and supervised new machine learning team - Overhauled ingestion and ETL pipelines using Dask, KubeFlow - Trained neural networks and statistical learners for the prediction of financial timeseries

Shadow is building the future of personal computers by providing gaming infrastructure as a service. I joined them with a double mission: to expand their data engineering stack, and to bring machine learning to the company. - Led AI/ML projects spanning anomaly detection, NLP, image augmentation, distributed high performance computing, timeseries forecast and tabular data prediction. - Expanded and maintained Shadow's data mining, ingestion and streaming pipelines using Scrapy, Snowflake, Kafka, Faust, ClickHouse. - Built scalable ETL and feature engineering pipelines using Dask. - Set up our ML/data science stack using GCP, Jupyter, TensorFlow, SKLearn, Optuna. - Deployed models to production and monitored performance using MLflow and Google AI Platform. - Mentored 5 analysts, data scientists and R&D engineers.

IQVIA is the world leader in healthcare information technologies. I had the pleasure to join their Predictive Analytics team, where I worked on developing new machine learning models for the early diagnosis of rare diseases. It was all going great. But then, Brexit happened. - Designed machine learning models for the predictive diagnosis of rare diseases among the US population. - Wrote and maintained Python packages automating the ETL, feature engineering and QC of terabyte-scale datasets. - Provided ad hoc statistical data analysis for US- and UK-based healthcare consulting teams.

Resonate's mission is to improve the efficiency, safety and sustainability of the rail industry using data science and machine learning. I worked with Resonate's Data team as a full-stack data scientist, splitting my time between designing, training and deploying predictive algorithms, and building up our data engineering stack. - Trained and deployed machine learning models for the forecasting and prevention of disruptive events on the National Rail network. - Designed and implemented end-to-end machine learning pipelines allowing for the automated data preparation, training and evaluation of ML models on EC2 instances. - Initiated a 4-people effort to refactor, optimise and document 5,000+ lines of legacy code, leading to performance improvements up to 200x and full compliance with Google coding standards. - Implemented and maintained data ingestion pipelines using AWS S3, Glue, Athena, QuickSight, and SPICE, delivering customer-facing dashboards and static reports.

Zopa is the premier peer-to-peer lending company in Europe. For my Faculty.ai Fellowship placement, I joined Zopa's data engineering team to work on the design and implementation of a state-of-the-art big data pipeline. - Implemented an interactive datamart connected to a cloud pipeline for the mining, storing, and analysis of financial data - Curated and funneled gigabyte-sized datasets into distributed data warehouses using AWS Glue, S3, Athena and Redshift - Liaised with the business analytics team to deliver interactive Tableau dashboards tracking key investment metrics in real time

The Faculty.ai Fellowship is a highly competitive postdoctoral fellowship training the best STEM PhD graduates across Europe to transition from academic research to data science and AI careers.