You will work directly with Camus' teams and external stakeholders to understand data, define the right questions, and translate messy real-world signals into reliable, production-grade data-driven analytics. You’ll bring a ground-truth perspective to product decisions and collaborate with the Engineering team to integrate ML models into our planning and operational workflows.

Responsibilities include designing, training, and evaluating predictive ML models for time-series forecasting; performing exploratory data analysis, feature engineering, and statistical modeling on large structured and unstructured datasets; defining ML infrastructure requirements and deploying models into operational workflows and decision-support tools; working cross-functionally to translate business objectives into ML solutions; communicating model performance, uncertainty, and limitations to both technical and non-technical audiences; promoting ML best practices around reproducibility, versioning, and testing.

What you’ll bring: PhD with 3+ years, Masters with 5+ years, or Bachelors with 8+ years in Machine Learning, Statistics, Computer Science, Applied Mathematics, or a related field; demonstrated track record of de

You will work directly with Camus' teams and external stakeholders to understand data, define the right questions, and translate messy real-world signals into reliable, production-grade data-driven analytics. You’ll bring a ground-truth perspective to product decisions and collaborate with the Engineering team to integrate ML models into our planning and operational workflows.

Responsibilities include designing, training, and evaluating predictive ML models for time-series forecasting; performing exploratory data analysis, feature engineering, and statistical modeling on large structured and unstructured datasets; defining ML infrastructure requirements and deploying models into operational workflows and decision-support tools; working cross-functionally to translate business objectives into ML solutions; communicating model performance, uncertainty, and limitations to both technical and non-technical audiences; promoting ML best practices around reproducibility, versioning, and testing.

What you’ll bring: PhD with 3+ years, Masters with 5+ years, or Bachelors with 8+ years in Machine Learning, Statistics, Computer Science, Applied Mathematics, or a related field; demonstrated track record of de