# Senior Data Scientist at Atos

Atos is hiring a Senior Data Scientist. Level rates it Builds AI ●●●●; you can [apply on Atos](https://jobsbylevel.com/go/e3a47c69-18d1-4f51-b9a0-3e39a3803075?utm_medium=ai-agent).

AI Level 4, AI centrality 87 out of 100. Timisoara.

## Details

- Company: [Atos](https://jobsbylevel.com/companies/atos)
- AI level: AI Level 4 (score 87 out of 100)
- Location: Timisoara
- Posted: September 28, 2026
- Apply: https://jobsbylevel.com/go/e3a47c69-18d1-4f51-b9a0-3e39a3803075?utm_medium=ai-agent

## Prepare for this job

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## Description

Bull is a story. One with a century of European innovation and a working environment where experts design powerful, sustainable, and sovereign digital solutions, enabling states and industries to retain full control over their data and their AI. Bull is also thousands of engineers, researchers and passionate tech people shaping the future of high‑performance computing, AI, and quantum technologies. Every day, our teams push the boundaries of what is technologically possible – from next‑generation HPC architectures to exascale supercomputers – supported by world‑class R&D, more than 1,600 patents, and unique end‑to‑end capabilities spanning hardware design, software engineering, data science and quantum research. We are a people‑centric, innovation‑driven company, where collaboration spans Europe, the Americas and India. We share a common vision of a responsible and sustainable innovation that delivers concrete impact for our customers. Senior Data Scientist Main Mission As a Senior Data Scientist at Bull, you'll lead the technical direction on client engagements and turn ambiguous business problems into working AI products. You'll set the approach, build the core of the solution yourself, and bring the rest of the team along with you. Working in a small squad of engineers, analysts, and PMs, you'll: Frame the problem: Sit with clients, cut through the ambiguity, and decide what's actually worth building up, including saying when ML isn't the answer. Own the solution design: Choose the modelling approach and architecture and justify the trade-offs to both engineers and executives. Raise the bar: Review code, mentor junior colleagues, and shape how we work across projects. This job opens up for a specific long-term project that will require the following tasks: Profile and assess data during feasibility — completeness, bias, label quality, whether the signal is there at all Recommend continue or kill, with the evidence written down Define the success metric at the gate, in terms the institution accepts and that will still mean something at scale Build evaluation harnesses and baselines; benchmark models against them Run bias and fairness testing, particularly where systems touch nationality, ethnicity or vulnerable groups Measure pilot results across sites and quantify what the difference implies for wider rollout Contribute to fundamental rights impact assessments alongside the governance function Support the build itself, especially model iteration and error analysis Our Projects & Technology Stack Real-world challenges across banking, insurance, manufacturing, pharmaceuticals, public sector and e-commerce, spanning call-centre logs, customer segmentation, IoT sensor streams, imagery, and web analytics. The scope is broad and keeps evolving as new data and technologies emerge. Tech we love : Python, SQL, Pandas, PySpark, Databricks, MLflow, PyTorch, Hugging Face, and the LLM tooling ecosystem. We deploy on Azure , AWS , Google Cloud Platform and on-premises. How we work : Small, focused teams (2–5 people), agile workflows, strong collaboration between engineers, analysts, scientists, and PMs. Key Skills Mandatory: 5+ years building data science or ML solutions, with a track record of work that reached real users. Python, SQL, NoSQL , and the standard analysis stack . Breadth across ML domains — we'd like to see genuine hands-on depth in at least two of: LLMs and generative AI, computer vision, classical ML. Production experience : you've deployed models — containers, pipelines, monitoring, retraining. You don't need to be an MLOps specialist, but you shouldn't need one to get something live. Comfort in the cloud (Azure, AWS, or GCP) and with distributed data processing (Spark/Databricks or equivalent). A strong mathematics or statistics background is preferred — you can reason why a model works, not just that it does. Client-facing confidence : you can explain a technical trade-off to a non-technical stakeholder and push back

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Source: https://jobsbylevel.com/jobs/senior-data-scientist-at-atos-ccd7a9

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