Description

You will lead the development and deployment of AI/ML solutions for anomaly detection.

Responsibilities

• Develop, optimize, and maintain computational models for debit transaction anomaly detection using AI/ML techniques.
• Design and implement statistical models, including standard deviation calculations, variance thresholds, and probabilistic models to enhance detection accuracy.
• Leverage machine learning algorithms (classification, clustering, time-series) to predict, detect, and manage anomalies.
• Integrate ML models into production systems in collaboration with engineering and business teams.
• Conduct performance monitoring, fine-tuning, and validation of ML models to ensure accuracy.

Required Skills

• 10+ years of hands-on experience in data science, AI, or ML engineering.
• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or Mathematics.
• Strong proficiency in Python, R, or Scala, with experience using libraries like Pandas, Scikit-learn, PyTorch, or TensorFlow.
• Solid understanding of statistical modeling, hypothesis testing, and regression analysis.
• Experience with anomaly detection techniques (supervised, unsupervised, hybrid).
• Expertise in working with large datasets using SQL or Spark.
• Experience deploying ML models into production environments (MLOps), preferably on AWS.
• Experience with Generative AI based implementations.

Key Skills

Education

Bachelor's or Master's degrees