Engineering Manager, Data Science (AdTech)

Fluent, LLC

4d•$180,000 - $225,000•Remote

About The Position

As our Engineering Manager, 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, with occasional travel to NYC.

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 Haves

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.

Responsibilities

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.
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.
Spearhead the exploration and adoption of autonomous agentic workflows to enhance decisioning and operational efficiency, moving beyond static models to self-correcting 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.
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.