👫About the team
At Equativ, we’re on a mission to develop advertising technologies that empower our customers to reach their digital business goals. This means that we rely on massively scalable, widely distributed, highly available, and efficient software systems; the platform deals with over 3 millions requests per second managed by 3,000 servers.
Our innovation team based in Paris, Nantes, Limoges, Krakow and Berlin is composed of 100+ straightforward and energetic engineers working in an Agile environment and ready to tackle the most complex technical challenges.
Your mission 👇
Join our Data Science team in Paris and help us design and deploy powerful deep-learning algorithms that enhance our products every day. We train our models on Google Cloud Platform with billions of logs and serve them in our own data centers. Our stack includes Google Dataflow, Vertex AI, and TensorFlow, powering neural networks that generate over 1 million predictions per second, each processed in under 2 milliseconds.
You'll get hands-on experience with a wide range of machine learning problems—classification, regression, time series, recommendation systems, generative AI—all powered by more data than you can imagine 😉. Your work will have a visible impact, fast.
What you’ll do ✏️
As part of the Data Science team, your responsibilities will include:
Improving existing algorithms and building new prediction models for large-scale, high-volume data scenarios
Identifying key performance issues and proposing innovative, data-driven solutions
Collaborating with team members to enhance our optimization and modeling systems
About you 💪
Minimum Qualifications:
Looking for a 6 months internship starting in September
Currently pursuing a Master’s degree in Applied Mathematics, Computer Science, or a related field (final-year or gap year)
Solid foundation in machine learning and mathematics
Proficient in Python
Strong analytical and problem-solving skills
Entrepreneurial mindset with a knack for spotting areas for improvement
Professional fluency in English, both written and spoken
Nice to Have:
Passion for reading, analyzing, and critiquing scientific papers
Personal ML/AI projects, especially with open-source contributions or shared code
A love for playing with massive datasets