Engineering Manager, Fluent

• 5+ years of experience in Data Science or ML Engineering, with at least 2 years managing or leading teams
• Strong ML fundamentals: classification, regression, clustering, and production ML deployment
• Experience with audience/customer modeling: propensity, lookalike, segmentation, or recommender systems
• Production ML experience: not just notebooks — deploying, monitoring, and maintaining models at scale
• Strong Python skills and experience with ML frameworks (scikit-learn, XGBoost, PyTorch, or similar)
• Proven people management skills: hiring, mentoring, and developing data science talent
• Excellent communication skills for translating technical concepts to business stakeholders and executives
• Experience with real-time scoring and feature stores

Desirable

• As our Engineering Manager - Data Science, you will lead the team responsible for driving business value through machine learning and advanced analytics
• You will own ROAS optimization, audience propensity modeling, and ML-powered capabilities that differentiate Fluent's advertising products and drive client success
• This role combines hands-on ML expertise with people leadership, requiring you to set technical direction for modeling initiatives while building and developing a high-performing data science team
• Drive ML/AI strategy: audience propensity models, lookalike modeling, and segmentation algorithms that improve campaign performance
• Own ROAS optimization: develop and refine models that maximize return on ad spend for clients
• Build production ML systems: feature engineering, model training, batch/online inference, and serving infrastructure
• Establish MLOps practices: model versioning, monitoring, drift detection, and reproducible training pipelines
• Champion experimentation: A/B testing frameworks, statistical rigor, and data-driven decision making
• Lead and grow the Data Science team: hiring, mentoring, performance management, and career development
• Partner with Product and Client Success to translate business requirements into ML solutions and communicate model capabilities
• Coordinate with Data Platform team to ensure reliable data foundations and feature pipelines for modeling
• Translate complex ML concepts into actionable insights for business stakeholders and executives
• Set technical direction and foster a culture of innovation, rigor, and continuous improvement