Senior Data Scientist at PGBPGNGLOBAL

✨ The Stack take on this PGBPGNGLOBAL job

• 🏢 This is PGBPGNGLOBAL's only job here right now
• 🛂 Visa: Non-EU work permit guide

Updated 2026-11-05, from our data and official sources.

Apply with your AI assistant

With The Stack connected to Claude, ChatGPT or Cursor, paste this. Your assistant tailors your CV and cover letter, checks the visa facts and gives you the apply link.

Help me apply to this job on The Stack: https://jobs.trythestack.co/jobs/senior-data-scientist-pgbpgnglobal

Not connected yet? Connect in a minute. To help your user apply, call the MCP tool prepare_application with slug "senior-data-scientist-pgbpgnglobal": it returns the apply link, the visa facts and a CV and cover letter checklist. To track jobs they saved or applied to, call check_jobs_open with their slugs.

About the role

Responsibilities:
- Translate ambiguous business problems into measurable analytics/AI objectives (hypotheses, success metrics, evaluation plans).
- Explore data, build features and develop models across ML, deep learning, RL and operations research methods (optimization, simulation).
- Design and apply Generative and Agentic AI where it adds value (copilots, decision-support, intelligent workflows).
- Partner with Data/AI Engineering to productionize models in cloud environments (GCP/Azure/Spark) following software engineering best practices.
- Own end-to-end delivery: prototyping, validation, deployment, monitoring and continuous improvement.
- Communicate findings and trade-offs to stakeholders; mentor colleagues and contribute to global data science standards.

Requirements:
- 5+ years in data science or equivalent; PhD with relevant research may substitute for experience.
- Strong programming in Python and SQL; experience with Spark or similar big-data stacks.
- Solid foundations in statistics, experimental design and ML algorithm development.
- Familiarity with operations research (optimization/simulation) is a strong advantage.
- Experience deploying scalable solutions to cloud platforms, and following Git, testing and CI/CD practices.
- Excellent communication and collaboration skills to influence cross-functional stakeholders.

Responsibilities

Design, build, and deploy scalable AI and analytics solutions to solve high-impact business problems. This includes framing ambiguous questions into technical problems and partnering with engineering teams to productionize models in cloud environments.

Requirements

Requires 5+ years of experience in Data Science or a PhD in a related AI/ML field. Candidates must be proficient in Python, SQL, and machine learning foundations with experience in cloud platforms like GCP or Azure.

Key skills

• Python — in 1200 other listings on The Stack
• SQL — in 545 other listings on The Stack
• Machine Learning — in 291 other listings on The Stack
• Generative AI — in 58 other listings on The Stack
• Agentic AI — in 39 other listings on The Stack
• Operations Research — in 5 other listings on The Stack
• Optimization — in 12 other listings on The Stack
• Simulation — in 5 other listings on The Stack
• Spark
• Cloud Computing
• Git
• CI/CD
• DevOps
• Feature Engineering
• Deep Learning
• Reinforcement Learning

On The Stack (as of 2026-11-05)

• This is PGBPGNGLOBAL's only listing on The Stack.
• 18 visa-sponsored jobs in Switzerland on The Stack, posted in the last 60 days.
• 68 visa-sponsored data scientist jobs in Europe on The Stack, posted in the last 60 days.

Frequently asked questions about PGBPGNGLOBAL

Is this Senior Data Scientist role still open?

It was posted on 2026-07-17. Listings more than 30 days old may no longer be active, so check PGBPGNGLOBAL's site before applying.

Does PGBPGNGLOBAL offer visa sponsorship?

This listing is marked as offering visa sponsorship. Eligibility rules are set by the employer, so check the details on PGBPGNGLOBAL's site.

Where is this role located?

The listing gives the location as Switzerland. It is marked on-site.

What skills does this role ask for?

The listing names: Python, SQL, Machine Learning, Generative AI, Agentic AI, Operations Research, Optimization, Simulation, Spark, Cloud Computing, Git, CI/CD, DevOps, Feature Engineering, Deep Learning, Reinforcement Learning.