Marketing Architects is looking to solve the problem of TV advertising being expensive, difficult to scale, and hard to measure by rebuilding the agency model with the client in mind and leveraging data science to deliver high-impact machine learning systems and decision intelligence.
Requirements
- Proficiency in Python (pandas, NumPy, scikit-learn; plus PyTorch or TensorFlow), SQL, and model lifecycle from exploration to production.
- Hands-on with cloud (AWS/Azure/GCP), Databricks, MLflow, Docker; insight with feature stores, monitoring, and CI/CD.
- Deep comprehension of experimental design, metrics, causal methods, and interpreting real-world impact.
- Exposure to real-time inference, streaming data, or reinforcement learning.
- Domain comprehension in our industry; privacy/ethics frameworks for ML in production.
Responsibilities
- Oversee development of supervised/unsupervised, NLP/CV, and forecasting models using Python (pandas, scikit-learn, PyTorch/TensorFlow) and SQL.
- Partner with MLE/Platform on MLOps (feature stores, MLflow, CI/CD, model monitoring, drift/decay, data quality SLAs).
- Ensure reliable deployment on cloud platforms (AWS/Azure/GCP), Databricks, Docker/Kubernetes.
- Champion data collection, labeling, and preprocessing standards; partner with Data Engineering on pipelines, observability, and lineage.
- Manage, mentor, and recruit data scientists; set goals, coach, and build a culture of experimentation, speed-to-market, and innovation.
- Establish DS/ML best practices (code review, design docs, experiment hygiene, model cards, documentation).
- Translate company priorities into a quarterly DS roadmap; define problem framing, success metrics, and stage-gates from research → production.
Other
- 7–10+ years in data science/ML with 2–4+ years leading or managing data scientists (hiring, performance management, career growth).
- Track record of scoping ambiguous problems, defining success metrics, and communicating trade-offs to execs.
- Demonstrated ability to set vision, build process, and foster an inclusive, high-performance team culture.
- Remote (except California)
- Full-time
