Data Science Team Lead (AI/ML/NLP)

Quick Facts

• Role: Data Science Team Lead (AI/ML/NLP)

• Work setup: Full remote or hybrid (international team)

• Team size: Lead 3–4 engineers (two teams available)

Description

Lead and mentor a data science team to build scalable data platforms and AI-driven solutions. You will design modern data architectures, develop large-scale NLP pipelines and data models, and deliver AI-powered data engineering and advanced analytics from concept to production. The role requires strong Python/SQL expertise and production-grade Generative AI/LLM workflows; NLP expertise is essential for one of the two teams.

Responsibilities

• Lead and mentor data science engineers

• Design and scale modern data platforms, including large-scale NLP pipelines and data models

• Drive AI-powered data engineering workflows, machine learning, and advanced analytics from concept to production

• Build solutions using Python, SQL, AWS, and modern data platforms such as Snowflake

• Apply statistical methods and predictive modeling to solve complex business challenges

• Develop scalable data workflows, automation, and analytical applications

• Collaborate with other teams and define technical standards and best practices across data and AI initiatives

Requirements

• 4+ years of experience in data science, machine learning, AI (including production-grade agentic AI workflows), or NLP

• NLP expertise is essential for one of the two teams

• Willingness to lead a team and mentor technical professionals

• Strong knowledge of AI, ML, data architecture, data modeling, and scalable data solutions

• Advanced hands-on Python and SQL

• Experience deploying machine learning or AI solutions into production

• Understanding of Generative AI, LLMs, or modern AI frameworks

• Fluent English communication skills (communicative Czech is an advantage)

Benefits

• Opportunity to shape modern data and AI solutions with real business impact

• Leadership role with ownership and technical influence

• Flexible working environment

• Continuous learning and access to modern technologies

• Competitive compensation and benefits