Role: Senior Data Scientist & Machine Learning Engineer Location- Dallas, TX - Remote Duration: Long term Contract We are seeking an autonomous, high-caliber Senior Data Scientist & Machine Learning Engineer to bridge the gap between advanced AI engineering and strategic business insights. In this role, you will take ownership of the entire ML lifecycle—from critical data analysis to architecting production-grade, Google Cloud Platform-based ML pipelines and advanced analytics systems. As a technical leader, you will translate ambiguous business problems into scalable, production-grade AI solutions, drive technical excellence, and mentor team members in engineering best practices. Core Responsibilities & Required Capabilities

• Technical Ownership & MLOps: End-to-end responsibility for designing, deploying, and monitoring scalable ML pipelines and AI solutions. You design from scratch , or convert experimental notebooks, into robust, production-ready code with automated CI/CD and feature management.
• Critical Analysis & Strategy: Autonomously dive into complex datasets to uncover growth opportunities, validate model assumptions, and deliver executive-level insights on business metrics, forecasting, and user behavior.
• Data Engineering & Collaboration: Act as a bridge to Data Engineering teams to co-design robust data architectures. You possess strong foundational skills in building optimized data pipelines, modeling clean schemas, and ensuring high-quality data ingestion for downstream ML models.
• Senior Leadership: Act as a strategic partner to product and business stakeholders, manage project delivery timelines, and champion rigorous code quality and best practices across the team.
• Preferred Domain Expertise: Strong preferred experience in Marketing Science, specifically around building and calibrating Marketing Mix Models (MMMs) and attribution frameworks to optimize ROI and budget allocation.

Tools and Technologies

• Google Cloud Platform (Google Cloud Platform)
• Data & Analytics: BigQuery (Advanced SQL, BigQuery ML), Cloud Storage
• AI & MLOps: Vertex AI (Pipelines, Model Registry, Feature Store), Agent Builders / Agent Platforms, Kubeflow
• Languages & Core Data Science
• Programming: Python (Expert), SQL (Advanced)
• Libraries: Scikit-Learn, XGBoost, LightGBM, TensorFlow, or PyTorch
• Preferred Marketing Science & Bayesian Modeling
• MMM Frameworks: Meridian, LightweightMMM
• Probabilistic Programming: PyMC, Stan, or similar Bayesian libraries
• Engineering & CI/CD

DevOps: Git, Docker, CI/CD pipelines, Airflow