Remote Data Science Tech Lead: Python & Ontology

BairesDev; Comisión de Fomento de Perú; Remote; ARS 184,752,000 - 277,128,000; Full time

Benefits offered by this job

Remote work (100%)]

USD compensation

Hardware and software setup for home

Flexible hours

Parental leaves and holidays

Mentorship and growth opportunities

Job summary

BairesDev is seeking a Data Science Technical Lead (Python + Ontology) to steer the design and delivery of a semantic data platform. You will guide technical decisions and set engineering standards, shaping how reference data becomes a trusted knowledge foundation.

Lead the development of domain ontology, Python pipelines, and ML components, while mentoring engineers and driving pragmatic modeling choices for scalable, production-grade systems.

Qualifications

• 6+ years of data science and ML engineering with leadership.
• Production Python pipelines for semantic processing.
• Expertise in ontology and semantic data modeling with RDF 1.1, OWL 2, SHACL.
• Experience designing a bounded domain ontology from a reference dataset.
• Advanced English proficiency.

Responsibilities

• Lead the technical direction of data science and ML work.
• Design a bounded domain ontology from a reference dataset.
• Build production Python pipelines for semantic processing and entity resolution.
• Integrate ontology services into the pipeline.
• Develop evaluation harnesses and solver adapters.
• Mentor engineers and set engineering standards across the team.

Skills

Data science

Machine learning engineering

Team leadership

Python production pipelines

Ontology knowledge

English proficiency

Tools

Python

RDF/OWL/SHACL

Job description

BairesDev is seeking a Data Science Technical Lead (Python + Ontology) to steer the design and delivery of a semantic data platform. You will guide technical decisions and set engineering standards, shaping how reference data becomes a trusted knowledge foundation.

Lead the development of domain ontology, Python pipelines, and ML components, while mentoring engineers and driving pragmatic modeling choices for scalable, production-grade systems.