The Data Science Team

We process billions of documents every day to alert our customers in case of data leaks! Our goal is to spot sensitive data in a massive amount of documents.

Introducing intelligence at the various levels of our processing pipeline is therefore a crucial issue for CybelAngel: we want to filter out non-sensitive elements. Our Data Science team has the mission to make our filtering algorithms as intelligent as possible, in order to optimize and facilitate the processing of security incidents delivered to our customers. Our Machine Learning Engineers build systems to measure models drift, build robust pipelines to ease production release and models retrain, and help the data scientists on the good practices of software development. Automatic retrain when we measure a drift in the performances will be the key project in the next few years! They are also in charge of designing Machine Learning models from data analysis to production release with the data scientists.

Your responsibilities

• Study and implement Data Science’s POCs

• Design Machine Learning algorithms

• Research and implement ML algorithms that seem to be relevant

• Conduct Machine Learning tests and experiments

• Train Machine Learning models required

• Improve existing packages

• Stay up-to-date with the latest advances in the field

• Classic ML and GenAI

Our (current) stack

• Language: Python, SQL

• Databases: ElasticSearch, BigQuery

• Librairies: Pandas/Polars, Scikit-learn, XGBoost, Matplotlib, Streamlit, Plotly, HuggingFace

• MLOps: Gitlab CI, Docker, Kubernetes, Terraform, ZenML, MLFlow, Kubeflow

• Environment: Jupyter notebook [gcp]

• Other: GCP, Datadog, Dataflow, Linear

Your preferred experience 

• A first experience as an ML Engineer or in a similar role 

• Good data modelling skills

• Ability to write robust code in Python

• Familiarity with standard libraries (Scikit-learn, XGBoost, pandas, numpy, …)

• Communication skills

• Ability to work in a team

• Good analytical and problem solving skills

• You already worked with a cloud provider (not necessarily GCP!)

• Organised

If you do not meet the requirements but you think you are a great fit, you are welcome to apply and explain why !

Our Product & Engineering Culture

🗃️ Small teams & short iterations

🤝 Culture of sharing, pair and mob programming, when it makes sense

👩‍🎨 Quality time dedicated to technical design

🧙‍♂️Half the software engineers have over 10 years of experience

🎒 Voyager Program to change team temporarily and discover how they work #becurious

✌️ Cooldown week dedicated to learning and experimenting

Our Benefits

💰 Salary range: 42-47k based on your level of seniority and competencies you will demonstrate during the hiring process

🏦 5 % bonus, based on company and individual performances

🍱 Debit card for paying for lunch, with company contribution

👩‍⚕️Healthcare insurance, with company contribution

🖥️Remote allowance

🤝 Referral bonus for every new CybelAngel we recruit based on your recommendation

🏋🏾‍♀️ Monthly credits to spend with CSE partner

🏝️5 weeks of holidays and RTT

🏅Subsidy for one sporting event per year

⚖️ Legal Care Service solution

🕠 Flexible work environment : flexible hours, remote (+45% of our team works fully remotely), right to disconnect charter, sick child leave paid

Diversity & Inclusion

💛 Advocate team

🌈 Very friendly and benevolent atmosphere

🇫🇷 Multicultural team (19 nationalities)

♀️34% women

👫 Index Egapro 84/100

We are committed to creating an inclusive workplace and giving everyone an equal opportunity to succeed, regardless of their background, identity, or life experiences. We value diversity and believe that every individual, no matter their journey, deserves a chance to contribute and thrive.

Your hiring journey with us

• 30’ call with Pierre, recruiter to make sure expectations from both ends have a good chance to match

• 30’ call with Fabien, Data Science Manager to evaluate skills and culture fit

• 60’ meeting with the whole Data Science team to present the technical test that you prepared at home => have the chance to present your methodology and added value