Title : Lead AI-ML Engineer
Location : Westerville, OH
Duration : Contract
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.