AI Data Scientist
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About Arlequin
Arlequin is building the sovereign European alternative to Palantir: an AI platform capable of processing massive data volumes without bias, with high-impact use cases such as fighting disinformation. Founded in late 2024 by Hugo Micheron (researcher in geopolitics and systemic risk) and Antoine Jardin (CNRS research engineer).~30 people, post-seed, Series A in progress. On-site in Paris.
The role
You’ll join our R&D team of 8, focused on researching, testing, and developing state-of-the-art algorithms that power HUDEX, our platform that lets non-technical analysts interpret narrative dynamics across large-scale digital ecosystems. You’ll work alongside experienced researchers, several of whom hold or are completing PhDs, across a range of fields (unsupervised learning, NLP, computer vision, topology, and more).
Stack
Technical stack:
Must have: Python and strong experience in data manipulation / data science / machine learning / development tools and libraries, Git/GitHub
Nice to have: LaTeX, MLflow
Methods & domains:
Must have: experience in NLP, working with high-dimensional unstructured data (text, computer vision, speech, multimodality…), clustering and statistics
Nice to have: experience with other unsupervised learning methods, deep learning, mathematical knowledge in graph and/or topology
What we're looking for
A strong scientific mindset and genuine intellectual curiosity
Able to read scientific literature and produce literature reviews
A high degree of autonomy
Versatility and adaptability: you’re comfortable researching across many fronts, switching between quick, pragmatic solutions and deeper long-term work, and occasionally stepping outside pure research to support engineering or client-facing needs
Quick to pick up new algorithms and put them into practice
Open to junior candidates
Professional English
Bonus
Academic publications
Already comfortable in a startup environment
Mastery of agentic coding tools and code models
Process (~2 weeks)
Screening (15 min)
Fit interview (30 min)
Technical interview (1h)
Founder culture fit (45 min)
Reference calls
Offer
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