Machine Learning Engineer

ING Hubs Poland is hiring!

The expected salary for this position: 13 000 - 18 000 PLN gross

The financial ranges specified in the announcement are adjusted and may differ from the range specified in the remuneration regulations

We are looking for you if you have:

• Proficiency in Python & SQL 
• Knowledge of ML frameworks (Scikit-learn, Pandas, NumPy) 
• Great analytical skills and ability to work with big data 
• Strong mathematical knowledge and logical thinking
• Knowledge of cybercrime threats (phishing, malware, sociotechnical attacks, skimming, etc.)
• Experience deploying models and integrating them into rule-based fraud detection and alert handling systems
• Excellent problem-solving, communication, and teamwork skills

You'll get extra points for:

• Experience optimizing code/compute and handling large datasets for fraud detection
• Master’s degree in a relevant field
• Certifications or experience in banking fraud or cyber security

Your responsibilities:

• Deploy and integrate machine learning models into fraud detection engines and alert handling systems
• Optimize, test, and maintain analytics code
• Collaborate with data scientists, engineers and rulewriters to improve efficiency of detection and alert handling.
• Contribute to a Scrum-based, international team focused on innovation and security

Information about the squad:

Machine Learning Engineer – deployment & model integration

Join our innovative team to support the deployment and seamless integration of machine learning models into fraud detection and alert handling systems for the banking sector. We seek a Machine Learning Engineer with strong data science skills, a deep understanding of fraud and cyber security, and a passion for operationalizing models.

If you’re passionate about operationalizing data science for fraud prevention and cyber security, we’d love to hear from you!

The role naming convention in the global ING job architecture will be “Engineer IV"

The financial ranges specified in the announcement are adjusted and may differ from the range specified in the remuneration regulations.