Data Science Engineer

We are seeking a skilled and driven Data Science Engineer to join our team focused on developing and deploying advanced fraud detection models for credit card transactions. This role will play a critical part in identifying fraudulent behavior in real-time, leveraging cutting-edge data science techniques and tools within the Azure ecosystem and Databricks environment.

Key Responsibilities:

• Design, develop, and deploy machine learning models to detect fraudulent credit card transactions.
  

• Analyze large volumes of structured and unstructured data to uncover patterns, trends, and anomalies related to fraud.
  

• Build scalable data pipelines and model training workflows using Databricks on Azure.
  

• Work closely with data engineering and platform teams to productionalize models and monitor performance.
  

• Continuously evaluate and refine models to improve accuracy, precision, and recall over time.
  

• Collaborate with cross-functional teams including fraud operations, engineering, and compliance.
  

Required Qualifications:

• 7+ years of experience in data science or machine learning engineering roles.
  

• Proven experience developing fraud detection models or working with financial transaction data.
  

• Strong programming skills in Python and SQL.
  

• Experience with Databricks, Spark, and Azure ML or related Azure data services.
  

• Solid understanding of machine learning techniques including supervised/unsupervised learning, anomaly detection, and model evaluation.
  

• Excellent analytical and problem-solving skills with a strong attention to detail.
  

Preferred Qualifications:

• Experience in the payments or financial services industry is a BIG plus.
  

• Experience in Microsoft Fabric is a plus.
  

• Familiarity with real-time data processing and stream analytics.
  

• Knowledge of model monitoring, drift detection, and model retraining strategies.