Data Science Engineer — Real-Time AI for Supply Chains (Hybrid)

Prewave; Wien; Hybrid; EUR 41.000 - 50.000; Vollzeit

Benefits dieser Stelle

Your birthday off

MyClubs partnership

Mental health support

Regular health check-ups

Company-wide training

Flexible hours

Hybrid working

Work from anywhere

Zusammenfassung

Prewave is seeking a Data Science Engineer to turn vast data into actionable intelligence from day one. You will apply NLP and ML to real-world supply chain risks and deploy production models.

You will own end-to-end data science work, collaborate with cross-functional teams, and continuously improve model performance, data sources, and production readiness.

Hybrid work in Austria with a competitive package and growth opportunities.

Qualifikationen

• Bachelor's degree or higher in CS, statistics, mathematics or related field.
• 3+ years of experience in developing, deploying and scaling ML models to production.
• Experience with NLP or ML technologies and tools like Torch or TensorFlow.
• Good experience in Python and Scala.
• Knowledge of SQL and relational databases.

Aufgaben

• Decode the data using NLP to extract meaning from diverse datasets.
• Formulate hypotheses, uncover patterns, and identify optimization opportunities.
• Build, fine-tune, and deploy ML models using Torch and TensorFlow.
• Bridge research with production-ready solutions.
• Monitor and improve model performance and accuracy.
• Expand the data universe by integrating new data sources.
• Collaborate with cross-functional teams to tackle business challenges with data-driven solutions.

Kenntnisse

NLP

Machine Learning

Python

Scala

SQL

Ausbildung

Bachelor's degree in Computer Science, Statistics, Mathematics or related fields

Tools

Torch

TensorFlow

Jobbeschreibung

Prewave is seeking a Data Science Engineer to turn vast data into actionable intelligence from day one. You will apply NLP and ML to real-world supply chain risks and deploy production models.

You will own end-to-end data science work, collaborate with cross-functional teams, and continuously improve model performance, data sources, and production readiness.

Hybrid work in Austria with a competitive package and growth opportunities.