Senior Data Scientist

Auxo Talent; Polska; Remote; PLN 240,000 - 360,000; Full time

Job summary

Auxo Talent is seeking a Senior Data Scientist to join our dynamic team on a 12‑month contract. This role is remote, enabling you to work from anywhere while collaborating closely with engineering and business partners.

The ideal candidate has strong applied ML fundamentals, hands-on experience building and evaluating models on real data, and proficiency in Python and SQL. Experience with embeddings, LLMs, NLP tasks, and transformer architectures is highly valued.

Qualifications

• Strong applied ML fundamentals with hands-on model development and evaluation on real data.
• Proficient in Python and SQL for data processing and modeling tasks.
• Experience with embeddings, semantic similarity, LLMs, or related AI techniques.
• Hands-on experience training supervised and unsupervised models, including NLP tasks.
• Knowledge of neural networks, transformer architectures, and model deployment considerations.
• Familiarity with ML frameworks such as TensorFlow, PyTorch, and PyCaret.
• Ability to retrain taxonomy classifiers or maintain production classification models.
• Ability to define baselines, metrics, test sets, and error analysis to show improvements.
• Ability to explain model behavior, tradeoffs, and edge cases to tech and business partners.

Skills

Applied machine learning

Python

SQL

NLP

LLMs

Embeddings

Model training

Explainability

Communication

Tools

TensorFlow

PyTorch

PyCaret

Job description

Our client currently seeks a Senior Data Scientist to join their dynamic team on a 12 month contract. This role is remote.

Required Skills

• Strong applied ML fundamentals, with hands‑on experience building and evaluating models on real data.
• Excellent Python and SQL skills.
• Practical experience with embeddings, semantic similarity, LLMs, or related AI techniques.
• Hands‑on experience training supervised and unsupervised models, including classification and NLP tasks.
• Working knowledge of neural network and transformer architectures.
• Proficiency with common ML frameworks such as TensorFlow, PyTorch, and PyCaret.
• Experience retraining a taxonomy classifier or maintaining classification models in production.
• Experimental judgment: able to define baselines, metrics, test sets, and error analysis that show whether quality improved.
• Ability to explain model behavior, tradeoffs, and edge cases clearly to engineering and business partners.