Data Scientist III - AI & Machine Learning
Builds and deploys scalable ML models using GCP tools like Vertex AI and BigQuery, managing full lifecycle from EDA to production MLOps. Requires 5+ years experience, expert Python/ML, and production deployment track record.
Responsibilities
- End-to-End Model Development: Design and implement the full ML lifecycle—from exploratory data analysis (EDA) and feature engineering to model selection, tuning, and validation.
- GCP Architecture: Leverage the full Google Cloud AI suite (Vertex AI, BigQuery ML, Dataflow, and Pub/Sub) to build robust, cloud-native ML solutions.
- MLOps & Engineering: Implement CI/CD for machine learning (CT - Continuous Training) to ensure models remain performant in production environments.
- Strategic Leadership: Partner with stakeholders to translate ambiguous business challenges into technical roadmaps. Mentor junior scientists and advocate for best practices in code quality and experiment tracking.
- Embed AI as a repeatable co-pilot in daily workflows by integrating experimentation into real work, and continuously refining its use with sound judgment and validation.
Requirements
- 5+ years in a professional Data Science role with a track record of deploying models at scale.
- Proficiency in Vertex AI, BigQuery, Cloud Storage, and Looker. Experience with Kubeflow is a major plus.
- Expert-level Python (pandas, scikit-learn, PyTorch/TensorFlow), Spark, and advanced SQL.
- Deep understanding of statistics and ML theory (e.g., Gradient Descent, Bias-Variance tradeoff, Bayesian inference).
- Experience with ETL/ELT processes, specifically using dbt.
Nice-to-Haves
- Infrastructure as Code: Comfortable with Terraform or similar tools.
- Strong curiosity for exploring new technologies, including AI.
Compensation
- Base salary: $126,000 to $149,000 upon hire, varying by experience, skills, certifications, and education.
- Annual bonus potential of 10% based on company performance.
- Equity grant with vesting schedule.
Tech Stack
- Data Warehouse: BigQuery
- Orchestration: Airflow (Cloud Composer)
- Modeling: Vertex AI Pipelines, JupyterLab
- Deployment: Docker, GKE (Google Kubernetes Engine)
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