Senior Data Scientist

About the Position

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.

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.
• 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, NoSQL, 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.

Requirements

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 when it matters. And you did this many times in the past.
• Strong applied statistics: experiment design, uncertainty, calibration, and the discipline to distinguish a real effect from a hopeful one.
• Experience with modern AI tools, RAG architectures, vector databases, and embedding models.
• Familiarity with LLM frameworks and libraries (e.g., LangChain, LlamaIndex).
• Experience evaluating ML systems against operational rather than academic criteria.
• Ability to communicate a negative result clearly to a non-technical audience and hold your position under pressure.
• You must speak Romanian natively and English advanced.

Nice to Have

• Exposure to regulated industries such as pharma, public sector, banking.
• 2+ years of experience in managing teams – you have managed teams for building, deploying, and maintaining an AI product/solution before.
• Databricks experience.
• OCR experience.
• Graph Neural Networks.
• Fairness and bias auditing methodology.
• Familiarity with the EU AI Act's high-risk evaluation and documentation requirements.
• Domain exposure to public administration, law enforcement, migration, or emergency services.
• Spatio-temporal modelling, survival analysis, or anomaly detection on log data.

We Offer

• Ownership of meaningful AI projects from first conversation to production.
• A team of strong engineers and scientists to build with - and to learn from.
• Flexibility, remote work options, and a culture that keeps senior people close to the code.
• Training and Certifications: Access to continuous learning and career development opportunities.
• Competitive salary and benefits package.
• Reimbursement: Get a yearly fixed amount for reimbursement.
• Performance Bonus: Earn an annual performance bonus based on your achievements.
• Career Advancement: Explore numerous opportunities for professional growth and career advancement.
• Extra Vacation Days: Take advantage of additional vacation days to relax and recharge.

Here, your ideas, your curiosity, and your technical excellence directly shape the next era of advanced computing - unlocking enterprise value, accelerating scientific progress, and driving positive impact for society.

Apply now »

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