Were hiring a hands-on Data Scientist with strong AI/ML engineering skills to help design and build AI/ML solutions across cloud platforms. You'll work with business and engineering partners to translate business problems into measurable, scalable AI/ML services - including GenAI agents, production ML models, and lightweight internal apps.
What you'll do
• Partner with business and engineering teams to translate problems into measurable AI/ML solutions and success metrics.
• Design, train, validate, and deploy models for classification, regression, recommendation, and time-series forecasting; pick algorithms, features, and evaluation strategies that match business goals.
• Develop and evaluate GenAI agent applications using frameworks like Langchain and Google ADK, leveraging techniques such as RAG, prompt engineering, and vector DB integration.
• Build and operate reliable data pipelines and model inference endpoints across GCP and AWS (BigQuery, Vertex AI, Cloud Run, S3, Lambda, SageMaker, etc.).
• Implement CI/CD, automated testing, and monitoring for ML/data projects (GitHub Actions, Cloud Build, CodeBuild/CodePipeline).
• Create lightweight dashboards and internal apps (Streamlit, Plotly Dash) to deliver models and insights to stakeholders.
• Write clear model documentation: problem formulation, modeling approach, validation, data needs, and deployment steps.
• Advocate for coding best practices, reproducibility, and shared documentation across a global data science organization.
Required qualifications
• Masters + 1+ years, or a PhD in Data Science, Computer Science, Applied Math/Statistics, Econometrics, or related quantitative field.
• Strong Python and SQL skills; experience with scikit-learn, XGBoost/LightGBM, and a deep learning framework (PyTorch or TensorFlow).
• Hands-on experience building GenAI/LLM applications and agentic workflows.
• Practical experience building reliable data pipelines and ensuring data quality/lineage on GCP (BigQuery).
• CI/CD experience for ML/data projects (e.g., GitHub Actions, Cloud Build, AWS CodeBuild/CodePipeline, etc).
• Practical experience building internal dashboards/apps with Streamlit and/or Plotly Dash.
• Cloud experience with GCP (BigQuery, Vertex AI, Cloud Run, Gemini Enterprise) and AWS (S3, Lambda, ECS, SageMaker).
• Solid comprehension of the mechanisms behind widely used AI/ML algorithms - including their intuition, assumptions, statistical theory, computational complexity, strengths/weaknesses, and when to use each.
Preferred qualifications
• Security and compliance best practices: IAM, secrets management, VPC/networking.
• Experience building APIs for model/agent inference.
• Containerization & orchestration.
• Workflow orchestration.
• Background in causal inference, statistics, or econometrics.
• A/B testing and experimentation design.
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