Simon C.

As one of the first pillars of Callyope, I lead the data infrastructure part, designing and building robust systems on the cloud following best practices, using mainly Python, Terraform and AWS.

• Designed and implemented a secure daily backup system to transfer files and database dumps from AWS to GCP.

• Built a data warehouse and an end-to-end ELT process using Python and DBT, enabling researchers to create robust training corpus datasets and the operational team to monitor clinical trials effectively.

• Developed a serverless architecture for computing tasks and a warehouse querying API, slashing infrastructure costs by over 90% while implementing near-instant auto-scaling and ensuring cloud-backed uptime.

• Implemented company-wide CI pipelines for Python/Terraform linting and introduced a Docker workflow, enhancing code readability and reducing deployment issues by over 80%, significantly cutting troubleshooting time from days to hours.

In charge of assisting a dozen Data Scientists from nothing to production.

• Built a custom job orchestrator tailored to data scientists’ needs to distribute computing with MapReduce using Python, MongoDB, EC2 and Docker, significantly speeding up computations and increasing cost efficiency by over 80%.

• Implemented a serverless API to interact with the job orchestrator, reducing API infrastructure cost by over 60% while ensuring near-instant auto-scaling, cloud-backed uptime, and secure authentication.

• Developed several monitoring solutions, including periodic checks on team services, which reduced bug detection time by over 80%, and improved access to machines Docker logs, cutting troubleshooting time by half.

• Contributed to the development of CI/CD pipelines, incorporating linters for best practices and automating deployment of the latest versions of infrastructure and applications from development to production, accelerating development time by 30% and reducing human errors by over 66%.

Implementation of data processing tools within Sensome, a startup originating from the École Polytechnique specializing in the creation of innovative connected medical devices.

Use and comparison of several cloud databases and selection of an architecture suited to the company's needs. Data analysis and search for correlations in reports provided by doctors to improve the product (a guide to help doctors recognize the type of clot formed during a stroke). Use of machine learning algorithms and image analysis through segmentation, all in Python.