Title: Data Science Engineer

Industry: Technology

Location: Culver City, CA

Duration: 18 months

Responsibilities

Design, develop, validate, and deploy machine learning models for marketing forecasting, campaign estimation, media planning, and budget optimization.
Build marketing response models incorporating response curves, diminishing-returns analysis, marginal efficiency, and budget allocation recommendations.
Develop time-series forecasting, resource estimation, predictive modeling, and Generative AI solutions, including LLM agents, retrieval systems, MCP servers, and entity resolution workflows.
Own the end-to-end machine learning lifecycle, including feature engineering, production deployment, monitoring, versioning, drift detection, retraining, and model evaluation.
Build and maintain production ML pipelines with Apache Airflow and Python services; collaborate with stakeholders to communicate modeling assumptions, results, and recommendations.

Requirements

Demonstrated experience developing and deploying machine learning solutions for marketing analytics, forecasting, estimation, or media optimization.
Advanced Python skills with pandas, NumPy, scikit-learn, and statsmodels, plus strong SQL and data warehouse experience.
Strong foundation in statistics, experimentation, uncertainty quantification, backtesting, model validation, and forecasting or probabilistic modeling using tools such as Prophet, PyMC, or Stan.
Hands-on experience managing production ML pipelines and the complete ML lifecycle, including deployment, monitoring, data quality checks, and retraining with Apache Airflow.
Experience building Generative AI solutions, including MCP servers, LLM agents, tool-calling workflows, prompt engineering, structured outputs, LLM evaluation, embeddings, vector databases, RAG, or entity resolution.

Skills