Data Scientist (ML/AI)

OEC; Polska; On-site; PLN 180,000 - 300,000; Full time

Job summary

OEC is seeking an experienced Data Scientist to lead machine learning solutions across the Mechanical business. You will own end-to-end ML workflows from problem framing to production deployment, delivering measurable business impact.

Focus areas include personalization, search and recommendations, forecasting, NLP, fraud detection, and GenAI experimentation. Collaboration with Product, Engineering, and leadership is essential to drive real results in a hybrid work environment.

Qualifications

• 3+ years of applied data science or machine learning experience.
• Proven success deploying production models that improved business metrics.
• Expertise in search, ranking, recommendations, and personalization.
• Forecasting, NLP, fraud/risk, anomaly detection, and experimentation.
• LLMs and AI evaluation frameworks.
• Strong Python, SQL, AWS, Airflow, and MLflow experience.
• Experience owning ML systems from development through production.
• Demonstrated technical leadership and stakeholder management.

Responsibilities

• Build and optimize search, ranking, recommendations, and personalization models.
• Develop forecasting models for demand, growth, conversion, and churn.
• Apply NLP and entity resolution to catalog and data repair.
• Design fraud detection, anomaly detection, and operational AI solutions.
• Run experiments, A/B tests, and causal analyses to measure impact.
• Prototype and evaluate LLMs and agentic AI solutions.
• Own models end-to-end from development through deployment and monitoring.
• Mentor other data scientists and establish modeling best practices.
• Partner with Product, Engineering, and business leaders to deliver measurable outcomes.

Skills

Applied ML

Production models

Python

SQL

AWS

Airflow

MLflow

Mentorship

Stakeholder mgmt

Education

Master's or PhD in quantitative field

Tools

Python

SQL

AWS

Airflow

MLflow

Job description

Lead the development of machine learning solutions across OEC's Mechanical business, spanning personalization, search and recommendations, forecasting, NLP, fraud detection, operational efficiency, and GenAI. This is an individual contributor role on OEC's Applied AI team, with end-to-end ownership from problem framing through production deployment and measured business impact.

What You'll Do

• Build and optimize search, ranking, recommendation, and personalization models.
• Develop forecasting models for demand, growth, conversion, and churn.
• Apply NLP and entity resolution to catalog, fitment, and repair data.
• Design fraud detection, anomaly detection, and operational AI solutions.
• Run experiments, A/B tests, and causal analyses to measure impact.
• Prototype and evaluate LLM and agentic AI solutions.
• Own models end-to-end, from development through deployment and monitoring.
• Mentor other data scientists and establish modeling best practices.
• Partner with Product, Engineering, and business leaders to deliver measurable outcomes.

What You Bring

• 3+ years of applied data science or machine learning experience.
• Proven success deploying production models that improved business metrics.
• Expertise in several of the following:
• Search, ranking, recommendations, and personalization
• Forecasting and predictive modeling
• NLP and text classification
• Fraud, risk, or anomaly detection
• Experimentation and causal inference
• LLMs and AI evaluation frameworks
• Strong Python, SQL, AWS, Airflow, and MLflow experience.
• Experience owning ML systems from development through production.
• Demonstrated technical leadership, mentorship, and stakeholder management.

Nice to Have

• Master's degree or PhD in a quantitative field.
• Experience in B2B commerce, marketplaces, or automotive.
• Experience with agentic AI systems, real-time inference, and complex product catalogs.

How We Work

• We value collaboration, ownership, and pragmatic problem-solving.
• We prioritize shared success over individual wins.
• We expect scientists to ship and own their work in production.
• We are measurement-first and evidence-driven.
• We encourage experimentation, continuous learning, and challenging assumptions.
• We leverage AI-assisted development while maintaining high standards for quality, reliability, and accountability.