Lead AI-ML Engineer - 10+ yrs- Westerville, OH -Onsite

Overview

On Site
Depends on Experience
Contract - W2
Contract - Independent
Contract - 6 Month(s)
100% Travel

Skills

AI
Mathematics
R
Pandas
Data Science
Spark
NumPy
PyTorch
scikit-learn
visualizations
clustering
statistics
optimize
MLOps
or Scala with experience using data science libraries (e.g.
hypothesis testing
regression analysis
unsupervised
analytical thinking
preferably on AWS
Key Responsibilities: Collaborate with stakeholders to understand business objectives and define requirements for anomaly detection. Develop
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
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
and reports to communicate findings effectively to business and technology stakeholders. Required Skills & Qualifications: Bachelors or Masters degree in Computer Science
or a related field. 10+ years of hands-on experience in data science
or ML engineering. Strong proficiency in Python
TensorFlow). Solid understanding of Data Science with a heavy focus on statistical modeling and Machine Learning
and variance modeling. Experience with anomaly detection techniques supervised
and hybrid approaches. Experience in Generative AI based implementations. Expertise in working with large datasets using SQL
or similar data-processing frameworks. Strong problem-solving
and communication skills. Experience in deploying ML models into production environments

Job Details

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

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