Senior Data Scientist — ML/LLM Lead for Production AI

Plume; Polska; Hybrid; PLN 180,000 - 280,000; Full time

Job summary

Plume seeks an experienced AI/ML leader to drive model development, data wrangling, and production-ready ML systems at scale. You will own end-to-end ML/LLM projects and collaborate with engineering and product teams to deliver robust models and insights across large datasets.

Applicants should have strong Python and SQL skills, experience with SageMaker or similar tools, and a track record of deploying ML solutions in production environments under tight timelines.

Qualifications

• Experience leading, developing and owning AI projects.
• Strong ML/LLM techniques and implementation experience.
• Proficient in Python with core data science libraries.
• Solid SQL skills for querying and data aggregation.

Responsibilities

• Lead, develop, maintain and improve ML/LLM projects and other automations.
• Manipulate, clean, and transform large-scale datasets of network and device telemetry for modeling and analysis.
• Design and implement feature engineering strategies to improve model accuracy and robustness.
• Use SQL and Python to explore data, build analytical pipelines, and deliver reproducible results.
• Translate product needs into analytical tasks and model development plans.
• Build and evaluate ML models for anomaly detection, forecasting, and optimization.
• Collaborate with engineering and product teams to productionize models and monitor performance.
• Balance experimentation with execution to deliver insights on tight schedules.
• Contribute to MLOps workflows for deployment, validation, and monitoring.

Skills

AI leadership

ML techniques

Python programming

SQL

Data wrangling

SageMaker

Statistics

Education

Bachelor's degree in CS/Math/Related

Tools

Python

Pandas

NumPy

Scikit-learn

SQL

SageMaker

Job description

Plume seeks an experienced AI/ML leader to drive model development, data wrangling, and production-ready ML systems at scale. You will own end-to-end ML/LLM projects and collaborate with engineering and product teams to deliver robust models and insights across large datasets.

Applicants should have strong Python and SQL skills, experience with SageMaker or similar tools, and a track record of deploying ML solutions in production environments under tight timelines.