Data Scientist: GenAI & ML for Operations & CX

Sonova; Warszawa; On-site; PLN 180,000 - 240,000; Full time

Benefits offered by this job

Flexible hybrid working model

Training opportunities and development

Growth opportunities with individual发展

Job summary

Sonova in Warsaw, Poland is seeking a Data Scientist to join on-site. The role drives data science and ML initiatives across Operations and CX, turning complex datasets into actionable insights to inform strategic decisions and improve efficiency.

You will design, develop, and deploy advanced data science and GenAI solutions, collaborate with global teams, and establish robust monitoring, experimentation, and governance practices to deliver measurable business impact.

Qualifications

• 5+ years of experience applying data science, ML, and predictive analytics to solve business problems.
• Proven experience delivering end-to-end data science projects from data prep through deployment.
• Strong knowledge of statistics, ML, experimentation, and model evaluation.

Responsibilities

• Conceptualize, develop, deploy, and drive adoption of data science, ML, and GenAI solutions with measurable business impact.
• Apply statistical and ML methods to diverse datasets to derive actionable insights.
• Translate business requirements into technical solutions and communicate findings clearly to stakeholders.
• Collaborate with analysts, engineers, and platform teams to ensure project delivery.
• Design AI-augmented workflows and establish best practices for AI-generated output.

Skills

Python

SQL

Databricks

PySpark

ML/AI

MLOps

English (Fluent)

Tools

Git

LLMs

Prompt engineering

Embeddings

Job description

Sonova in Warsaw, Poland is seeking a Data Scientist to join on-site. The role drives data science and ML initiatives across Operations and CX, turning complex datasets into actionable insights to inform strategic decisions and improve efficiency.

You will design, develop, and deploy advanced data science and GenAI solutions, collaborate with global teams, and establish robust monitoring, experimentation, and governance practices to deliver measurable business impact.