Senior Applied Scientist, Machine Learning

McAfee•Frisco, TX

23h•Hybrid

About The Position

Role Overview: We are seeking a Senior Applied Scientist to join McAfee's Consumer ML team and drive AI-powered solutions that deliver personalized experiences, optimize pricing, and improve payment success for millions of global customers. In this role, you will lead the end-to-end development and deployment of high-impact ML models across fraud detection, dynamic pricing, journey optimization, and contextual recommendation systems. You'll design and execute experimentation frameworks, champion GenAI tooling adoption to accelerate development, and apply advanced techniques, including deep learning and reinforcement learning. This is a hands-on technical leadership role requiring 8+ years of Applied ML experience, proven expertise in personalization, pricing optimization, or churn/propensity modeling for digital subscriptions, and strong cross-functional collaboration skills to translate ML innovation into measurable business outcomes. This is a Hybrid position located in Frisco, TX. You will be required to be on-site on an as-needed basis; when you are not working on-site, you will work from your home office. You must be within commutable distance of Frisco, TX. We are not offering relocation assistance at this time. About the Role Strategic Vision: Drive the ML science strategy for pricing, recommendation systems, and personalized consumer experiences, to maximize McAfee’s customer value. Model Development: Lead the research, implementation, and delivery of Applied AI/ML models using user behavior and subscription data to enhance personalization and product value. Optimization & Experimentation: Lead algorithm development to optimize consumer journeys, increase conversion rates, and drive monetization strategies. Design and execute controlled experiments (A/B and multivariate tests) to validate and enhance model performance. Generative AI Enablement: Leverage GenAI tools—such as GitHub Copilot, Claude Code, and other AI coding assistants—to amplify development productivity in data preparation, model tuning, and orchestration workflows. Champion the integration of GenAI capabilities into the ML lifecycle to accelerate experimentation and reduce time-to-market. Research & Knowledge Sharing: Stay at the forefront of ML science, contributing to the development of new algorithms and applications. Share knowledge through internal presentations, publications, and participation in academic or industry forums. Reinforcement Learning is a Plus: Guide the team in applying reinforcement learning methods such as contextual bandits, SARSA, and Q-learning. Implement exploration-exploitation strategies, including epsilon-greedy, Thompson sampling, and Upper Confidence Bound (UCB) to optimize decision-making for pricing and recommendation engines. Cross-Functional Collaboration: Partner with Marketing, Product, Sales, and Engineering teams to ensure ML solutions align with strategic objectives and deliver measurable business impact. About You Experience: 8+ years of expertise in Applied AI & ML, complemented by at least 3 years of technical leadership experience mentoring machine learning scientists in technical capacities. Mandatory Qualification: Proven track record in at least one of the following: implementing AI/ML-based personalized messaging techniques to enhance consumer/customer product experiences; developing AI/ML-based dynamic pricing and personalized offer strategies for pricing optimization; or creating customer/consumer churn and propensity models specifically for digital subscription use cases Technical Expertise: Deep proficiency in classical ML and deep learning techniques (e.g., XGBoost, Random Forest, SVMs, deep neural networks), autoencoders, representation learning, and deep recommender system techniques, as well as reinforcement learning methods (contextual bandits, SARSA, Q-learning). Strong programming skills in Python, SQL, and ML frameworks. Tooling & Libraries: Proficient with ML libraries such as PyTorch and Scikit-learn, with a strong background in feature engineering, model validation, and evaluation metrics. Mathematical Foundations: Solid understanding of the mathematical and statistical principles underpinning ML algorithms (linear algebra, calculus, probability) and a passion for solving complex problems through research and application of emerging techniques. Communication & Collaboration: Excellent communicator who can distill complex ML concepts for both technical and non-technical stakeholders and collaborate effectively across cross-functional teams to align ML models with business goals. #LI-Hybrid Company Overview McAfee is a leader in personal security for consumers. Focused on protecting people, not just devices, McAfee consumer solutions adapt to users’ needs in an always online world, empowering them to live securely through integrated, intuitive solutions that protects their families and communities with the right security at the right moment. Company Benefits and Perks: We work hard to embrace diversity and inclusion and encourage everyone at McAfee to bring their authentic selves to work every day. We’re proud to be Great Place to Work® Certified in 10 countries, a reflection of the supportive, empowering environment we’ve built where people feel seen, valued, and energized to reach their full potential and thrive. We offer a variety of social programs, flexible work hours and family-friendly benefits to all of our employees. Bonus Program Pension and Retirement Plans Medical, Dental and Vision Coverage Paid Time Off Paid Parental Leave Support for Community Involvement We're serious about our commitment to diversity which is why McAfee prohibits discrimination based on race, color, religion, gender, national origin, age, disability, veteran status, marital status, pregnancy, gender expression or identity, sexual orientation or any other legally protected status. McAfee recognizes and supports its obligation to reasonably accommodate applicants and employees with disabilities. We are here to help. Please let us know if you need a reasonable accommodation for any part of the application, interviewing, hiring, or at any other time during the employment process. Please do not include personal medical information in the email. Diversity is foundational for our business success. We want to be a workplace of choice for all people and we value the unique perspectives offered by a diverse workforce. McAfee does not unlawfully discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, citizenship, disability, protected veteran status, age, ancestry, medical condition, genetic information, marital status, pregnancy, or any other legally protected status. This principle applies to all areas of employment: recruitment and hiring, training, performance evaluations, promotions and transfers, compensation and benefits, and social and recreational programs. McAfee desires to be an employer of choice with an inclusive environment for all individuals. As part of this goal and in compliance with various laws and regulations, McAfee provides reasonable accommodation to applicants and employees. Requests for reasonable accommodation for applicants and employees are evaluated on a case-by-case basis. By submitting your CV/resume, McAfee will use the personal data that you have submitted in order to consider your application for a relevant role. McAfee processes personal data in accordance with its Privacy Policy (https://www.mcafee.com/enterprise/en-gb/about/legal/privacy.html).

Requirements

8+ years of expertise in Applied AI & ML, complemented by at least 3 years of technical leadership experience mentoring machine learning scientists in technical capacities.
Proven track record in at least one of the following: implementing AI/ML-based personalized messaging techniques to enhance consumer/customer product experiences; developing AI/ML-based dynamic pricing and personalized offer strategies for pricing optimization; or creating customer/consumer churn and propensity models specifically for digital subscription use cases
Deep proficiency in classical ML and deep learning techniques (e.g., XGBoost, Random Forest, SVMs, deep neural networks), autoencoders, representation learning, and deep recommender system techniques, as well as reinforcement learning methods (contextual bandits, SARSA, Q-learning).
Strong programming skills in Python, SQL, and ML frameworks.
Proficient with ML libraries such as PyTorch and Scikit-learn, with a strong background in feature engineering, model validation, and evaluation metrics.
Solid understanding of the mathematical and statistical principles underpinning ML algorithms (linear algebra, calculus, probability) and a passion for solving complex problems through research and application of emerging techniques.
Excellent communicator who can distill complex ML concepts for both technical and non-technical stakeholders and collaborate effectively across cross-functional teams to align ML models with business goals.

Nice To Haves

Reinforcement Learning is a Plus: Guide the team in applying reinforcement learning methods such as contextual bandits, SARSA, and Q-learning. Implement exploration-exploitation strategies, including epsilon-greedy, Thompson sampling, and Upper Confidence Bound (UCB) to optimize decision-making for pricing and recommendation engines.

Responsibilities

Drive the ML science strategy for pricing, recommendation systems, and personalized consumer experiences, to maximize McAfee’s customer value.
Lead the research, implementation, and delivery of Applied AI/ML models using user behavior and subscription data to enhance personalization and product value.
Lead algorithm development to optimize consumer journeys, increase conversion rates, and drive monetization strategies.
Design and execute controlled experiments (A/B and multivariate tests) to validate and enhance model performance.
Leverage GenAI tools—such as GitHub Copilot, Claude Code, and other AI coding assistants—to amplify development productivity in data preparation, model tuning, and orchestration workflows.
Champion the integration of GenAI capabilities into the ML lifecycle to accelerate experimentation and reduce time-to-market.
Stay at the forefront of ML science, contributing to the development of new algorithms and applications.
Share knowledge through internal presentations, publications, and participation in academic or industry forums.
Guide the team in applying reinforcement learning methods such as contextual bandits, SARSA, and Q-learning.
Implement exploration-exploitation strategies, including epsilon-greedy, Thompson sampling, and Upper Confidence Bound (UCB) to optimize decision-making for pricing and recommendation engines.
Partner with Marketing, Product, Sales, and Engineering teams to ensure ML solutions align with strategic objectives and deliver measurable business impact.