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Fluent is building the next-generation advertising network, Partner Monetize & Advertiser Acquisition. Our vision is to build an ML/AI-first network of advertisers and publishers to achieve a common objective — elevating relevancy in E-commerce for everyday shoppers. As our Engineering Manager - AdTech Data Science, you will lead the team responsible for driving business value through machine learning and advanced analytics in the Adtech space. You will own ROAS optimization, audience propensity modeling, and deep learning powered capabilities that differentiate Fluent's advertising products and drive client success. We are seeking expert-level knowledge of sequence modeling (Transformers/Attention) and generative approaches for user behavior, moving beyond legacy logistic regression to implement deep architectures that solve for data sparsity and long-term user value. 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. This role is fully remote in the United States or Canada (Ontario), with occasional travel to NYC.
Job Responsibility:
Drive the Deep Learning Evolution: Lead the architectural transition from legacy tree-based models (XGBoost) to advanced Neural Network architectures (Deep Learning) for audience propensity, lookalike modeling, and real-time segmentation
Own Value-Based Bidding (ROAS): Evolve our bidding strategy from simple conversion prediction to sophisticated ROAS-based optimization, developing models that predict user value (LTV) to maximize client returns within dynamic auction environments
Champion Agentic AI & Automation: Spearhead the exploration and adoption of autonomous agentic workflows to enhance decisioning and operational efficiency, moving beyond static models to self-correcting systems
Build Production Deep Learning Systems: Oversee the end-to-end engineering of high-scale inference pipelines, including embedding layers, real-time feature stores, and low-latency serving infrastructure such as ONNX, TensorRT etc
Advance MLOps & Experimentation: Establish rigorous MLOps practices for model versioning and drift detection while shifting further into multi-armed bandit strategies (Exploration vs. Exploitation) that optimize directly for business outcomes (Revenue/GP) rather than just model metrics
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.
Requirements:
PhD (preferred) or Master’s Degree in Computer Science, Mathematics, or other Quantitative Field
8+ years of experience in Data Science or ML Engineering, with at least 2 years managing or leading technical teams
AdTech Deep Learning Architecture Expertise: Deep hands-on experience with modern ranking and retrieval architectures (e.g., DLRM, DCNv2, Two-Tower), with a focus on multi-objective learning (MMoE) to jointly optimize for clicks, conversions, and revenue
Strategic Ownership of Agentic AI: Demonstrated passion for and aptitude in defining the roadmap for autonomous agentic systems. You must be ready to learn, champion, and own the evolution from static RAG to production-grade agentic orchestration
Real-Time Inference & Engineering: Experience deploying complex models into high-throughput, low-latency production environments (familiarity with ONNX, TensorRT, or feature stores)
Commercial & Business Acumen: Ability to translate improved model performance (AUC/LogLoss) into tangible business metrics (GP, RPM) and prioritize R&D efforts based on ROI and unit economics
Strong Python skills with a focus on Deep Learning frameworks (PyTorch, TensorFlow) as well as traditional ML libraries (XGBoost, scikit-learn)
Proven people management skills: Experience hiring, mentoring, and developing high-performing data science talent
Excellent communication skills for translating technical concepts to business stakeholders and executives.
Nice to have:
Hands-on Agentic Framework Experience: Familiarity with graph-based orchestration such as LangGraph, multi-agent systems (CrewAI), or emerging standards like Model Context Protocol (MCP)
ROAS optimization and campaign performance modeling
Databricks and MLflow experience
Experience with audience/customer modeling: propensity, lookalike, segmentation, or recommender systems.
What we offer:
Competitive compensation
Ample career and professional growth opportunities
New Headquarters with an open floor plan to drive collaboration
Health, dental, and vision insurance
Pre-tax savings plans and transit/parking programs
401K with competitive employer match
Volunteer and philanthropic activities throughout the year
Educational and social events
The amazing opportunity to work for a high-flying performance marketing company!