ML Engineer Sr Consultant

Visa•Austin, TX

6d•$163,400 - $261,500•Hybrid

About The Position

As a Machine Learning Engineer at the Senior Consultant / Senior Manager level, you will design and develop integrated software algorithms to structure, analyze, and leverage data in product and systems applications across structured and unstructured environments. You will use machine learning and statistical modeling techniques, including decision trees, logistic regression, and Bayesian analysis, to develop and evaluate algorithms that improve product and system performance, quality, data management, and accuracy. Rigorous validation and identification of logical flaws using sound machine learning practices are essential to support the integrity and accuracy of deployed solutions. You will translate algorithms and technical specifications into code using current programming languages and technologies. The role includes designing and implementing adaptive learning methods with controls on effectiveness, methods for explaining model decisions where necessary, model validation, and A/B testing. All roles require digital fluency, including the ability to work with emerging technologies such as Generative AI tools (e.g., ChatGPT and Microsoft Copilot) to support everyday work. Visa requires at least three days in the office; expectations for these days will be confirmed by the Hiring Manager. The position is performed in an office setting and requires travel 5–10% of the time. Work hours vary based on department needs.

Requirements

Senior Consultant: 8+ years of relevant work experience with a Bachelor's Degree; at least 5 years with an Advanced Degree (e.g., Master's, MBA, JD, MD); 2 years with a PhD; or 11+ years of relevant work experience.
Bachelor's Degree in Computer Science, Operations Research, Statistics, or a highly quantitative field, or equivalent experience, with strength in deep learning, machine learning, data mining, statistical or other mathematical analysis.
Relevant coursework in modeling techniques such as logistic regression, Naïve Bayes, SVM, decision trees, or neural networks.
Ability to program in one or more scripting languages such as Perl or Python and one or more programming languages such as Java, C++, or C#.
Experience with one or more common statistical tools such as SAS, R, KNIME, or Matlab.
Digital fluency, including ability to work with emerging technologies such as Generative AI tools.

Nice To Haves

9 or more years of relevant work experience with a Bachelor's Degree, 7 or more years with an Advanced Degree (e.g., Master's, MBA, JD, MD), or 3 or more years with a PhD.
Experience developing and programming integrated software algorithms for structured and unstructured data environments.
Experience applying statistical modeling techniques such as decision trees, logistic regression, and Bayesian analysis.
Experience rigorously validating machine learning solutions and identifying logical flaws.
Experience translating algorithms and technical specifications into code using current programming languages and technologies.
Experience designing and implementing adaptive learning methods, model validation, and A/B testing.
Experience explaining model decisions and ensuring post-deployment integrity and accuracy.
Experience working with large datasets and modern big data and ML/AI technologies.
Experience collaborating with cross-functional teams to define requirements and execute machine learning initiatives.
Deep learning experience with TensorFlow.

Responsibilities

Develop and program integrated software algorithms for product and systems applications in structured and unstructured environments.
Apply machine learning and statistical modeling techniques to improve product and system performance, quality, data management, and accuracy.
Conduct rigorous validation and identify logical flaws using sound machine learning practices.
Translate algorithms and technical specifications into code using current programming languages and technologies.
Design and implement adaptive learning methods, model validation, A/B testing, and explainability of model decisions.
Support the post-deployment integrity and accuracy of machine learning solutions.