Key Figures 

• 20 years of experience in data science 

• 500+ data scientists, all consultants 

• 5 offices in Paris, London, New York, Hong Kong, and Shanghai 

• 350+ clients (CAC40, Fortune500) 

• $1M+ in profit generated for our clients since 2006 

• 1,000+ data science projects 

Within Ekimetrics, the Innovation Department works on AI research topics in collaboration with our industrial and academic partners. The department comprises several PhD experts in generative AI, deep learning, computer vision, time series, explainability, and causality. Each expert leads teams tasked with studying and building state-of-the-art methods and adapting them to specific business problems, developing new methodologies or algorithms to address identified challenges, and ensuring a seamless handover for integration within Ekimetrics' industrial ecosystem. 

A central focus of the Innovation Department is closing the gap between cutting-edge AI agents and the day-to-day reality of delivering data science at scale. The department is investing in agentic systems that accelerate the data science lifecycle while keeping data scientists firmly in control of quality, governance, and business relevance. This internship sits at the heart of that effort, combining applied AI research with production-grade engineering on Ekimetrics' modern data stack. 

Internship Objective: From a Business Question to a Trusted Recommendation 

LLM-based agents have made rapid progress in the past two years: they can now write, run and tune machine learning code with little human help. Yet real data science projects rarely start from a clean, well-defined task. They start from a business question and imperfect data, and the early choices (what to predict, how to measure success, how to validate) decide whether the result can be trusted. Automating these steps calls for assistants that know when to ask, when to stop, and how to back every claim with evidence. 

The Innovation Department is building remit, a harness that semi-automates the data science workflow. At each step of a project (framing, data design, exploratory analysis, modeling, evaluation), AI agents draft the output, automatic checks catch common mistakes such as data leakage, and a data scientist reviews and accepts the result before moving on. Ultimately, a business user brings a question and a dataset, and the system frames the problem, prepares and explores the data, compares models against a simple baseline, and returns a clear recommendation in which every figure can be traced back to the data. 

The work builds on recent research in agentic data science [1]. Agents now earn medals on many of the Kaggle competitions in MLE-bench [2], yet they tend to guess silently rather than ask when a task is ambiguous [3]. Meanwhile, tabular foundation models such as TabICLv2 [4] give strong predictive models without any tuning. 

The intern will work with senior data scientists and AI builders to build and refine remit. This internship offers an opportunity to engage with cutting-edge AI research and production engineering. 

Key Responsibilities 

The intern will: 

• Review the latest research on agentic data science and on the evaluation of AI agents, building on the team's recent review. 

• Design and implement remit's agents, starting with problem framing and data design, together with the automatic checks that catch mistakes such as data leakage. 

• Help define how the system works with business users: when to ask a clarifying question, when to stop rather than guess, and how to explain results in plain language. 

• Extend the system toward modeling and evaluation, building on trusted tools (scikit-learn, AutoML, tabular foundation models) rather than free-form generated code. 

• Build a benchmark of realistic business cases, run experiments to measure quality, reliability and cost, and take part in a pilot with business users. 

• Help integrate the system with the Databricks stack (Unity Catalog, MLflow) and support the safe and governed operation of the agents. 

Profile 

• Currently in the final year of a Master's or Engineering degree, with strong foundations in machine learning, statistics, and programming. 

• Good grasp of model validation: train/test splits, data leakage, baselines. 

• Strong analytical and problem-solving skills, with the ability to work autonomously on an open-ended research-and-build problem and to explain results to non-specialists. 

• Familiarity with large language models and agentic techniques (prompting, tool calling, structured outputs, evaluation). 

• Strong Python skills and a solid command of the standard data science toolkit (pandas/polars, scikit-learn, gradient-boosting libraries). 

• Fluency in English. 

• Nice to have: experience with an agent framework, solid software engineering practices (git, code review, testing), and exposure to Databricks, MLflow or cloud data platforms. 

Benefits 

• Hands-on Research & Engineering Experience: Build a pioneering agentic system on real data science workflows, contributing to a tool with direct real-world impact. 

• Collaborative Learning: Engage with a team of data scientists, AI experts, and PhDs in a dynamic, research-driven environment within Ekimetrics' Innovation Department. 

• Skill Development: Gain expertise in LLM agents, the evaluation of AI systems, and production tooling on Databricks (Unity Catalog, MLflow). 

• Career Growth: Opportunity to transition into a full-time Data Scientist role in AI at Ekimetrics, or into a PhD program, upon successful completion of the internship. 

• Scientific Contribution: Opportunity to co-author and publish scientific papers in reputable AI and data science conferences and journals. 

• Networking: Collaborate with Ekimetrics' industrial and academic partners, enhancing your professional network in the AI research community. 

• Ethical and Sustainable AI: Learn to develop AI solutions that align with ethical, governed, and sustainable best practices, a core commitment of Ekimetrics. 

Duration: 6 months, Paris 

References 

[1] LLM-Based Data Science Agents: A Survey of Capabilities, Challenges, and Future Directions (2025). https://arxiv.org/abs/2510.04023 

[2] MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering (2025). https://arxiv.org/abs/2410.07095 

[3] Ambig-DS: A Benchmark for Task-Framing Ambiguity in Data-Science Agents (2026). https://arxiv.org/abs/2605.09698 

[4] TabICLv2: A Better, Faster, Scalable, and Open Tabular Foundation Model (2026). https://arxiv.org/abs/2602.11139