Title: Lead AI-ML Engineer

Location: Westerville, OH

Key Responsibilities:

Collaborate with stakeholders to understand business objectives and define requirements for anomaly detection.

Develop, optimize, and maintain computational models for debit transaction anomaly detection using AI/ML techniques.

Perform data analysis, generate insights, and identify patterns to support decision-making.

Design and implement statistical models, including standard deviation calculations, variance thresholds, and probabilistic models to enhance anomaly detection accuracy.

Work with existing models to apply backtracking methodologies and improve anomaly reduction strategies.

Leverage machine learning algorithms (e.g., classification, clustering, time-series modeling) to predict, detect, and manage anomalies.

Collaborate with engineers and business teams to integrate models into production systems.

Conduct performance monitoring, fine-tuning, and validation of ML models to ensure accuracy and reliability.

Prepare technical documentation, visualizations, and reports to communicate findings effectively to business and technology stakeholders.

Required Skills & Qualifications:

Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related field.

10+ years of hands-on experience in data science, AI, or ML engineering.

Strong proficiency in Python, R, or Scala with experience using data science libraries (e.g., NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow).

Solid understanding of Data Science with a heavy focus on statistical modeling and Machine Learning, hypothesis testing, regression analysis, and variance modeling.

Experience with anomaly detection techniques - supervised, unsupervised, and hybrid approaches.

Experience in Generative AI based implementations.

Expertise in working with large datasets using SQL, Spark, or similar data-processing frameworks.

Strong problem-solving, analytical thinking, and communication skills.

Experience in deploying ML models into production environments, MLOps, preferably on AWS