Machine Learning Engineer- Senior Consultant Level

Visa•Austin, TX

41d•$173,000 - $276,900•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 both structured and unstructured environments. You will use machine language and statistical modeling techniques such as decision trees, logistic regression, Bayesian analysis, and others to develop and evaluate algorithms that improve product/system performance, quality, data management, and accuracy. Rigorous validation and identification of logical flaws via sound machine learning practices are essential to support the post-deployment phase integrity and accuracy of solutions. You will translate algorithms and technical specifications into code using current programming languages and technologies. The role includes designing and implementing methods for adaptive learning (self-learning models) with controls on effectiveness, methods for explaining model decisions where necessary, model validation, and A/B testing of models. All roles require digital fluency, including the ability to work with emerging technologies such as Generative AI tools (e.g. ChatGPT, Microsoft Copilot) to support everyday work.

Requirements

8 or more years of relevant work experience with a Bachelor Degree or at least 5 years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 2 years of work experience with a PhD.
Experience in developing and programming integrated software algorithms for structured and unstructured data environments.
Ability to program in one or more scripting languages such as Rust or Python and one or more programming languages such as Java, C++ or C#.
Advanced Generative AI Experience/Usage

Nice To Haves

9 or more years of relevant work experience with a Bachelor Degree or 7 or more relevant years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 3 or more years of experience with a PhD
Bachelor's Degree in Computer Science, Operations Research, Statistics or highly quantitative field (or equivalent experience) with strength in Deep Learning, Machine Learning, Data Mining, Statistical or other mathematical analysis.
Experience in applying statistical modeling techniques such as decision trees, logistic regression, Bayesian analysis, and others.
Experience in rigorous validation and identification of logical flaws in machine learning solutions.
Experience in translating algorithms and technical specifications into code using current programming languages and technologies.
Experience in designing and implementing adaptive learning methods, model validation, and A/B testing.
Experience in explaining model decisions and ensuring post-deployment integrity and accuracy.
Experience in working with large datasets and modern big data and ML/AI technologies.
Experience in relevant coursework in modeling techniques such as logistic regression, Naïve Bayes, SVM, decision trees, or neural networks.
Experience with one or more common statistical tools such as SAS, R, KNIME, Matlab.
Deep learning experience with TensorFlow is a plus.

Responsibilities

Develop and program integrated software algorithms for product and systems applications in both structured and unstructured environments.
Apply machine language and statistical modeling techniques to improve product/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 methods for adaptive learning, model validation, A/B testing, and explainability of model decisions.