Knowledge of data transformation frameworks like dbt
This role offers the opportunity to work end-to-end on ML and data projects within a small, high-impact team at the intersection of data engineering and data science
Job Summary
This role offers the opportunity to work end-to-end on ML and data projects within a small, high-impact team at the intersection of data engineering and data science.
Candidates will be responsible for implementing scalable ELT/ETL pipelines and applying MLOps best practices for model packaging, deployment, and monitoring.
The position provides exposure to cloud platforms, modern ML frameworks, and the chance to deliver data products that directly influence business decisions.
Matching Summary
This role offers the opportunity to work end-to-end on ML and data projects within a small, high-impact team at the intersection of data engineering and data science.
Skills & Requirements
Must-have
Proficiency in Python and SQL
Experience with ELT/ETL pipelines
Knowledge of data transformation frameworks like dbt
Familiarity with core ML frameworks such as scikit-learn or PyTorch
Understanding of MLOps concepts including CI/CD
Nice-to-have
Experience deploying ML models to production
Domain knowledge in insurance or reinsurance analytics
Familiarity with BI tools like Looker or Tableau
Interest in NLP and text-based ML tools
Prior experience in technology consulting or startup environments
Key Requirements
Degree in Computer Science, Engineering, Statistics, Mathematics, or related quantitative field
Professional experience in data engineering, data science, or ML engineering
Hybrid work requirement: onsite at least three days per week