Cyril Lay

Machine Learning Engineer - Contractor


About

Hey there, I am Cyril Lay, welcome to my website. Let me introduce myself quickly below !

I work as a software engineer contractor, specialized in data. I provide services for conceiving, creating or enhancing diverse applications and programs.

Fond of computers since my early age, I obtained a degree in computer science, with a specialization in data mining. In order to learn how to use my machine learning knowledge on today’s high volumes of data, I worked as a data engineer and ML engineer for a year and a half, before starting to work independently.

Although I am specialized in data and back-end systems, I can work full stack and eventually pick-up new technologies on the go.

Reach out at cyril@lays.pro !

Skillset

Data engineering Data science Cloud
Scala, Spark, SQL, Hadoop, Kafka, Airflow Python, Keras, SK-Learn, Open-cv, Fast.ai, R AWS (S3, EMR, ECS, EC2, Lambda, Redshift), Docker, Unix/Bash

References

“Cyril enthusiastically approaches complex problems and is able to think through them clearly. Cyril is a good listener and seeks out feedback to improve. He works well in teams and is able to explain complicated ideas succinctly to team members on the business and technical sides. I was impressed by how quickly Cyril learned and how self directed he was. Cyril was also involved in some presentations to partners and clients where he represented the company very well. Cyril is a promising machine learning engineer and I would gladly work with him again in the future.” - Jonathan Baker, Simudyne

“Cyril and I were both at GumGum together working on data-engineering and backend development. He quickly showed great aptitude and a drive for results. Eventually becoming a full time member of the team, he consistently produced results and became a key asset. I would work with him again and recommend him to any software engineering or data related role.” - Azam Abdulkadir, Gumgum Inc.

Services

  • Creation of end-to-end machine learning pipelines with your data
  • Training automation, deployment (cloud/on-premises) and monitoring
  • Improving your existing pipelines : boost your models performances (training, accuracy and inference)
  • Adding monitoring tools for your production applications
  • Work with your data scientists to scale their code and increase their productivity and efficiency, generate training datasets

Latest work

  • Fixing and deploying a chabot marketplace on IBMCloud Foundry (The Conversation Consultancy)
  • Built internal big data pipelines for business intelligence (MyTraffic)
  • Helped in developing a high-scale ad trafic forecasting tool (Gumgum Inc.)
  • Deployed monitoring and management tools for a group of Kafka clusters (Gumgum Inc.)