Description

You will lead the development and maintenance of anomaly detection systems using advanced AI/ML techniques.

Responsibilities

• Define requirements for anomaly detection by collaborating with business stakeholders.
• Develop, optimize, and maintain computational models for debit transaction anomaly detection.
• Design and implement statistical models, including variance thresholds and probabilistic models.
• Apply backtracking methodologies to existing models to improve anomaly reduction.
• Monitor, fine-tune, and validate ML models in production environments.

Required Skills

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

Key Skills

Education

Any Graduate