Automotive AI Developer & Data Scientist — ML Pipelines

Hitachi Vantara Corporation; Kraków; On-site; PLN 180,000 - 240,000; Full time

Job summary

GlobalLogic in Kraków seeks a Medior AI Developer & Data Scientist to build and deploy an AI-driven Measurement Analytics Tool for high-volume automotive data.

You will own ML pipelines, feature extraction, and reporting interfaces, collaborating with senior architects and junior developers to deliver production-ready solutions.

The role emphasizes autonomous contribution, data integration from textual logs and numeric telemetry, and bridging engineering data with actionable insights.

Qualifications

• 2–4 years in data science, ML, or software eng. with ML models
• Proficient in Python and data-science ecosystem; rapid UI prototyping
• Experience applying time-series analysis, clustering, NLP/LLMs to real datasets
• Strong data ingestion/processing of structured and unstructured data
• Knowledge of vehicle software verification or CAN/LIN is a plus
• BS/MS in CS/Data Science/Engineering or related field

Responsibilities

• Build, maintain, and optimize ML pipelines for high-volume automotive data
• Preprocess and structure heterogeneous telemetry streams to extract patterns
• Train, evaluate, and fine-tune analytics models (time-series, clustering, NLP/LLMs)
• Write clean, modular Python code and create intuitive UI/API interfaces
• Collaborate with senior architects and domain experts to implement features

Skills

Python

Data Science

ML models

Time-series

NLP/LLM

Data integration

Automotive domain

Education

B.Sc. or M.Sc. in Computer Science/Data Science

Tools

Pandas

NumPy

scikit-learn

Job description

GlobalLogic in Kraków seeks a Medior AI Developer & Data Scientist to build and deploy an AI-driven Measurement Analytics Tool for high-volume automotive data.

You will own ML pipelines, feature extraction, and reporting interfaces, collaborating with senior architects and junior developers to deliver production-ready solutions.

The role emphasizes autonomous contribution, data integration from textual logs and numeric telemetry, and bridging engineering data with actionable insights.