Data Science Engineer

Make a profound impact on how millions of people connect with technology globally by shaping the future of marketing intelligence. In this dynamic role, you will design, build, and productionize next-generation machine learning and Generative AI components that directly drive strategic decisions, optimize campaign performance, and unlock novel insights for a world-renowned technology leader.

Aquent is thrilled to partner with a globally recognized technology leader that stands at the forefront of innovation, continuously shaping how millions worldwide interact with technology and digital experiences. This visionary organization is dedicated to pushing boundaries, crafting intuitive products, and delivering unparalleled value to its vast customer base through cutting-edge advancements in artificial intelligence and data science. By joining forces with Aquent, you will gain access to an extraordinary opportunity to bring innovative concepts to life, from ideation to seamless deployment, and contribute to products that are truly at the leading edge.

The Opportunity

We are seeking a visionary professional eager to drive significant business impact by building advanced intelligence products. You will collaborate closely with cross-functional analytics experts to refine modeling approaches, manage the full model lifecycle, and serve as a primary technical resource. This is an unparalleled opportunity to work with cutting-edge AI infrastructure, build intelligent agents, and develop sophisticated forecasting models that redefine industry standards.

What You'll Do

• Design, build, and productionize machine learning and Generative AI components for critical applications such as media planning, campaign estimation, and intelligence products.
• Develop sophisticated forecasting, estimation, and marketing response models, including dynamic budget-allocation recommendations informed by response curves and diminishing-returns analysis.
• Construct advanced Generative AI components, including intelligent agents, robust retrieval mechanisms, and sophisticated entity resolution systems, implementing rigorous evaluation frameworks to ensure reliable and consistent LLM output.
• Independently manage the full model lifecycle, encompassing initial problem framing, meticulous feature engineering, comprehensive model development, thorough validation, deployment as Python services and automated pipelines, and continuous monitoring with strategic retraining.
• Collaborate closely with internal analytics experts to refine modeling approaches and rigorously validate results, ensuring accuracy and business relevance.
• Serve as a primary point of contact for ML engineering support during business hours.

Required Qualifications

• Proven experience in building and deploying machine learning models specifically for marketing use cases, including expertise in time-series forecasting, effort/resource estimation, and advanced media-mix style response curve and diminishing-returns (saturation) modeling.
• A strong understanding of core marketing performance concepts such as budget allocation, marginal efficiency, campaign pacing, and incrementality.
• Exceptional Python skills, demonstrating proficiency with essential libraries including pandas, NumPy, scikit-learn, statsmodels, and at least one forecasting or probabilistic modeling library (e.g., Prophet, PyMC, Stan).
• A solid grounding in statistics and experimentation principles, including uncertainty quantification, robust backtesting methodologies, and comprehensive model validation techniques.
• Hands-on experience building and operating batch ML pipelines, specifically within Apache Airflow, covering intricate scheduling, efficient backfills, data quality checks, and automated retraining processes.
• Demonstrated ability to independently own the full model lifecycle, from initial concept to production deployment, including advanced feature engineering, meticulous model versioning, proactive drift monitoring, and effective retraining feedback loops.
• A strong understanding of developing Generative AI components, including expertise in AI infrastructure components, tool design, governed semantic layers, LLM agents, intricate tool-calling workflows, advanced prompt engineering, and structured outputs.
• Familiarity with various LLM evaluation frameworks (e.g., creating golden datasets, LLM-as-judge methodologies, and regression testing).
• Proficiency with vector databases and embeddings for Retrieval Augmented Generation (RAG), and designing entity resolution across diverse systems.
• Working knowledge of SQL and practical experience with enterprise-grade data warehouses (e.g., Redshift).
• Exceptional communication and collaboration skills, with the ability to clearly articulate complex model assumptions, intricate trade-offs, and actionable results to both highly technical and non-technical stakeholders.

Nice to Have Qualifications

• Experience developing robust services and APIs in Python.
• Proficiency with PySpark for efficient large-scale feature processing.
• Practical experience with Bayesian modeling techniques.
• Knowledge of advanced optimization techniques and constrained budget allocation strategies.