Data Science Engineer

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. Join us in connecting exceptional talent with an enterprise that truly redefines industry standards.

Are you a visionary professional eager to shape the future of marketing intelligence and drive significant business impact? We are seeking a talented individual to join a dynamic team where you will be instrumental in designing, building, and productionizing the next generation of machine learning and Generative AI components. Your groundbreaking work will directly influence strategic marketing decisions, optimize campaign performance, and unlock novel insights, making a tangible difference in how this leading company connects with its audience globally. This is an unparalleled opportunity to bring innovative concepts to life, from ideation to seamless deployment, and contribute to products that are truly at the leading edge.

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 Subject Matter Experts (SMEs) 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.

What You'll Bring

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, rigorous 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 and tool design, particularly with a focus on governed semantic layers for natural-language data access.
  • Practical experience with LLM agents, intricate tool-calling workflows, advanced prompt engineering, and generating 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, fostering effective partnerships with analytics SMEs.

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.