About the role

Engineering Manager - Data Science leading a team for machine learning and advanced analytics. Driving business value in Fluent's next-gen advertising network with a focus on E-commerce.

Responsibilities

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.

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.