Machine Learning & Model Development:

• Design, develop, and deploy scalable machine learning models using Google Cloud Platform services (Vertex AI, BigQuery ML, etc.).
• Implement efficient feature engineering processes from various data sources.
• Ensure model accuracy, fairness, and robustness throughout the lifecycle.
• Optimize model performance for inference speed and compute utilization.

Marketing Mix Modeling (MMM) & Advanced Analytics:

• Build, run, and maintain robust Marketing Mix Models (MMMs) to evaluate media channel performance and optimize marketing spend for clients.
• Implement and customize open-source MMM frameworks, such as Google Meridian or Meta Robyn, to align with client’s specific business requirements and datasets.
• Design and implement statistical experiments (A/B testing) for optimal product and feature evaluation.
• Collaborate with data engineers and marketing managers to understand their requirements and provide analytical solutions.

SQL & Feature Extraction:

• Write complex SQL queries and scripts to extract, explore, and analyze training data in BigQuery.
• Optimize analytical queries for performance and efficiency at scale.

Python & ML Frameworks:

• Develop custom Python applications to automate model training and deployment processes.
• Leverage Python libraries (e.g., scikit-learn, pandas, etc.) for data modeling and analysis.

Model Monitoring and MLOps:

• Implement monitoring and alerting systems to proactively identify data drift and resolve model degradation.
• Investigate and troubleshoot model-related incidents in production.

Documentation:

• Maintain comprehensive documentation for all ML models, experimental results, and MLOps processes.

Essential:

• Proven experience with Google Cloud Platform (GCP) and its AI/data services (Vertex AI, BigQuery).
• Strong SQL skills, with expertise in BigQuery SQL for large datasets.
• Proficiency in Python programming, with experience in machine learning and data science libraries.
• Experience in designing, training, and deploying statistical and machine learning models.
• Demonstrated comfort and hands-on experience running Marketing Mix Models (MMMs), specifically utilizing modern open-source frameworks like Meridian or Robyn.
• Proficiency in version control systems and collaborative Git-based development practices (e.g., branching strategies, pull requests, and code reviews). 

Preferred:

• Google Cloud Platform certification (e.g., Professional Machine Learning Engineer).
• Experience with MLOps and workflow orchestration tools (e.g., Kubeflow, Apache Airflow).
• Familiarity with data visualization tools (e.g., Looker, Data Studio, PowerBI).
• Practical skills and experience in Generative AI, including designing and building AI agents, working with Large Language Models (LLMs), or developing autonomous workflows.