Data Science Engineer

Aquent is partnering with a globally recognized technology leader that is at the forefront of innovation, shaping the way millions interact with technology and digital experiences every day. This organization is dedicated to pushing boundaries, creating intuitive products, and delivering unparalleled value to its vast customer base through cutting-edge advancements in artificial intelligence and data science.

Are you a visionary Data Science Engineer 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 work will directly influence strategic marketing decisions, optimize campaign performance, and unlock new insights, making a tangible difference in how this leading company connects with its audience globally. This is an incredible opportunity to bring innovative concepts to life, from ideation to deployment, and contribute to products that redefine industry standards.

What You’ll Do

• Design, build, and productionize machine learning and Generative AI components for media planning, campaign estimation, and intelligence products.
• Develop sophisticated forecasting, estimation, and marketing response models, including budget-allocation recommendations based on response curves and diminishing-returns analysis.
• Construct advanced Generative AI components, including agents, retrieval mechanisms, and entity resolution systems, implementing robust evaluation frameworks to ensure reliable and consistent LLM output.
• Independently manage the full model lifecycle, from initial problem framing and feature engineering to model development, validation, deployment as Python services and Airflow pipelines, and continuous monitoring and retraining.
• Collaborate closely with internal analytics Subject Matter Experts (SMEs) to refine modeling approaches and validate results.
• Serve as a primary point of contact for ML engineering support during business hours (PST).

What You’ll Bring

Required Qualifications

• Proven experience building and deploying ML models for marketing use cases, including time-series forecasting, effort/resource estimation, and media-mix style response curve and diminishing-returns (saturation) modeling.
• Strong understanding of marketing performance concepts such as budget allocation, marginal efficiency, pacing, and incrementality.
• Strong Python skills, proficient with libraries such as pandas, NumPy, scikit-learn, statsmodels, and a forecasting or probabilistic modeling library (e.g., Prophet, PyMC, Stan).
• Sound grounding in statistics and experimentation, including uncertainty quantification, backtesting, and model validation.
• Hands‑on experience building and operating batch ML pipelines in Apache Airflow, covering scheduling, backfills, data quality checks, and retraining.
• Demonstrated ability to own the full model lifecycle independently, from concept to production, including feature engineering, model versioning, drift monitoring, and retraining feedback loops.
• Good understanding of developing Generative AI components, including:
  • AI infrastructure components and tool design, with a governed semantic layer for natural-language data access.
  • LLM agents, tool-calling workflows, prompt engineering, and structured output.
  • LLM evaluation frameworks (e.g., golden datasets, LLM-as-judge, regression testing).
  • Vector databases and embeddings for Retrieval Augmented Generation (RAG), and entity resolution across systems.
• Working knowledge of SQL and experience with data warehouses (e.g., Redshift).
• Exceptional communication and collaboration skills, with the ability to explain complex model assumptions, trade‑offs, and results to both technical and non-technical stakeholders, and to work effectively with analytics SMEs.

Nice to Have Qualifications

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