Leadership & Team Development: Lead and mentor a high-performing team of ML scientists. Foster a collaborative culture that promotes innovation, continuous learning, and technical excellence.
Strategic Vision: Drive the ML Science strategy for pricing, recommendation systems, and personalized consumer experiences, aligning efforts with McAfee’s objectives to optimize customer value.
Model Development: Oversee the design, implementation, and delivery of ML models using user behaviour and subscription data to enhance personalization and product value. Familiarity to traditional and classical ML is a plus.
Reinforcement Learning Implementation: Guide the team in applying reinforcement learning techniques, such as contextual bandits, SARSA, and Q-learning. Implement exploration-exploitation strategies like epsilon-greedy, Thompson sampling, and Upper Confidence Bound (UCB) to optimize decision-making frameworks for pricing and recommendation engines.
Cross-Functional Collaboration: Work closely with teams across Marketing, Product, Sales, and Engineering to ensure that ML solutions align with strategic objectives and deliver measurable business impact.
Optimization & Experimentation: Lead the team in creating algorithms for optimizing consumer journeys, increasing conversion rates, and driving monetization strategies. Design and execute controlled experiments (A/B and multivariate tests) to validate and improve model performance.
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.
Requirements & Skills:
8+ years of experience in machine learning, with 3+ years in a leadership role managing ML scientists. You’ve demonstrated the ability to drive technical innovation and mentor teams.
Expertise in classical ML and deep learning techniques (XGBoost, Random Forest, SVM, deep neural networks, etc.), reinforcement learning techniques (contextual bandits, SARSA, Q-learning), and proficiency in Python, SQL, and ML frameworks.
You are proficient with ML libraries like PyTorch, Scikit-learn, and others. You have a strong background in feature engineering, model validation, and evaluation metrics.
You possess a deep understanding of the mathematical and statistical principles behind machine learning algorithms (e.g., linear algebra, calculus, probability) and are driven by solving complex problems. You have a track record of researching and applying new ML techniques to solve real-world challenges.
You are an effective communicator who can explain complex ML concepts to technical and non-technical stakeholders. You excel in collaborating with cross-functional teams to align ML models with business goals.
