Senior Data Scientist / ML Engineer

About Hellowork Group

Hellowork group is the leading French digital player in employment, recruitment, and training. Through its various solutions, the group supports active individuals throughout their professional lives, companies, recruitment agencies, temporary employment agencies on their HR and recruitment challenges, and training centers/schools on the valorization of their training offers.

With 600 employees and €124 million in orders in 2024, the Hellowork group's services are used by 80,000 professionals and 5 million users* each month. They enable over 7,000 recruitments and 500 training enrollments each day.

At Hellowork Group, we develop digital solutions for employment and training, used daily by millions of people and companies.

Behind these products, there is a lot of data, machine learning in production, and an increasingly important place for generative AI and LLMs, with a simple objective: to create concrete and useful uses.

In this context, we are recruiting a Senior Data Scientist / ML Engineer for a central role, at the crossroads of modeling, MLOps, and product engineering. The position is within a Data Science team of 6 people, with diverse and complementary profiles, and covers the entire lifecycle of models, from modeling to production.

Missions

A role at the heart of data and AI topics, where we naturally move from exploration to technical decisions... then to production (yes, the real one)!

Modeling & Experimentation

• Design, improve, and challenge machine learning models on various use cases: ranking, recommendation, NLP, LLM
• Quickly test new approaches through POCs and benchmarks
• Evaluate models from technical, business, and product perspectives

Production & Industrialization

• Deploy models in production and support their integration into products
• Implement reliable and reproducible pipelines
• Monitor performance over time and strengthen the robustness of ML systems

MLOps Structuring & Senior Role

• Evolve the team's MLOps practices
• Define and maintain production standards
• Contribute to tooling, architecture choices, and structuring technical decisions

LLM & Generative AI

• Work on concrete use cases: prompting, fine-tuning, RAG, agents
• Identify the most relevant approaches and those that truly create value
• Transform useful experiments into industrialized product uses

Transverse Work & Product Impact

• Collaborate closely with product, data, and engineering teams
• Participate in the scoping and prioritization of topics
• Share learnings and disseminate ML best practices

Stack & Environment

Depending on the topics, the stack combines modeling, NLP / LLM, production deployment, and MLOps.

• Languages & data: Python, SQL, Elasticsearch
• NLP / LLM: Hugging Face, Transformers, embeddings, RAG
• Infra & tooling: APIs, Git, CI/CD
• MLOps: model tracking, monitoring, and observability tools

Qualifications

• Someone comfortable with machine learning in production topics
• Someone capable of intervening in both modeling and production deployment
• An autonomous, structured profile with a real focus on quality, robustness, and impact
• An ability to take a step back, make the right technical choices, and advance the team's practices