Data Scientist
At a glance
Mid-level Data Science role at TechBiz Global GmbH. Remote, Poland · full-time.
Pay not stated Data Scientist salary
remoteroles summary, based on the employer's posting.
What you'll do
- Train and refine large and small language models on GPU systems, then tune them for better performance
- Build machine-learning pipelines from data preparation through model training and deployment
- Clean and organize real-world datasets, including logs, telemetry, time-series and streaming data
- Choose training methods and tools, then track model quality, speed, scalability and resource use
- Explain technical decisions and resolve model, GPU and data-pipeline issues with engineering, data and business teams
What you bring
- Professional work as a data scientist, machine-learning engineer, AI engineer or in a comparable role
- Hands-on experience training or fine-tuning LLMs or SLMs with GPUs, plus strong Python skills
- Experience with PyTorch, TensorFlow or Hugging Face Transformers, and knowledge of GPU training environments
- Understanding of model training from data preparation and tokenisation through evaluation and optimisation
- You are based in Poland and can discuss prior AI projects, technical challenges and results clearly in English
Who this fits
This role suits a hands-on mid-level data scientist who can take language-model training from data preparation through production work. You need to be based in Poland.
From the employer
At TechBiz Global, we provide recruitment services to top clients from our international portfolio. We are currently looking for a Senior Data Scientist with strong hands-on experience in training AI models, particularly Large Language Models (LLMs) and Small Language Models (SLMs), using GPU infrastructure and real-world datasets.
The ideal candidate should be based in Poland and able to clearly demonstrate their technical expertise, explain the tools and frameworks they use, and describe the complete model-training process—from data preparation to deployment and performance optimisation.
Key Responsibilities
Train, fine-tune, and optimise LLMs and SLMs using GPU infrastructure.
Build and manage end-to-end machine learning training pipelines.
Prepare, clean, structure, and process large volumes of real-world data.
Select appropriate models, frameworks, tools, and training approaches based on project requirements.
Apply techniques such as supervised fine-tuning, transfer learning, prompt tuning, and parameter-efficient fine-tuning.
Monitor model performance and improve accuracy, speed, scalability, and resource utilisation.
Work with structured, unstructured, time-series, telemetry, log, and streaming data.
Clearly document and explain the tools, methods, and technical decisions used throughout the model-training process.
Collaborate with engineering, data, and business teams to move models from experimentation into production.
Troubleshoot issues related to model quality, training stability, GPU performance, and data pipelines.
Proven professional experience as a Data Scientist, Machine Learning Engineer, AI Engineer, or similar role.
Strong hands-on experience training or fine-tuning LLMs and/or SLMs.
Practical experience using GPUs for AI model training.
Strong Python programming skills.
Experience with machine learning and deep-learning frameworks such as:
PyTorch
TensorFlow
Hugging Face Transformers
Experience with GPU-related technologies and environments, such as CUDA, distributed training, cloud GPU platforms, or GPU clusters.
Strong understanding of model-training workflows, including data preparation, tokenisation, model selection, training, evaluation, and optimisation.
Ability to clearly explain previous AI projects, tools used, technical challenges, and achieved results.
Experience working with large and complex datasets.
Good English communication skills.
Based in Poland.
Relevant Industry Experience
Experience in one or more of the following industries or data environments would be highly valuable:
E-commerce
Finance or banking
Insurance
Healthcare or medical data
Telemetry and IoT data
Application or system logs
Real-time and streaming data
High-volume enterprise data environments
Nice to Have
Experience with distributed model training.
Experience with LoRA, QLoRA, PEFT, quantisation, or model compression.
Experience with MLOps tools and model deployment.
Knowledge of Docker, Kubernetes, MLflow, or similar technologies.
Experience using AWS, Azure, or Google Cloud for AI workloads.
Experience deploying AI models into production environments.
Knowledge of data privacy, security, and governance requirements.
Data Science work from LinkedIn, X, Reddit and Upwork, in one list.
Each morning you get everything that fits, strongest first.
Data ScienceRemote
Find me workBy the team behind remoteroles.