Cordial - Data Scientist - Production Engineering
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Requirements
• Bachelor’s degree or higher in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field, plus 3+ years of experience working with real-world, industry, or production data in a data science, applied ML, or analytics role • , plus 3+ years of experience working with real-world, industry, or production data • Demonstrated experience contributing to production data science or analytics systems, not only exploratory or academic projects • production data science or analytics systems • Strong programming skills in Python and experience writing maintainable, production-quality code • Experience working with large datasets and performance-sensitive workflows • large datasets • Prior experience with data pipelines and orchestration frameworks (e.g., Dagster, Airflow, etc.) • data pipelines and orchestration frameworks • Cloud platform expertise, particularly AWS services (Glue, Athena, ECS, S3 Tables, etc.) for scalable data processing and model deployments • Hands-on experience with modern data warehouse solutions (Snowflake, BigQuery, etc.) including query optimization, clustering strategies, and cost management • data warehouse solutions • query optimization • Experience with big data technologies and distributed computing frameworks for handling enterprise-scale event datasets • big data technologies • Solid understanding of data science fundamentals, including statistics and modeling concepts, sufficient to work closely with research-oriented data scientists • Ability to work independently and ramp up quickly in an existing codebase and system • Experience working in small, fast-moving teams where ownership and autonomy are expected • small, fast-moving teams
Responsibilities
• Optimize existing data science models and systems for performance, scalability, and reliability • performance, scalability, and reliability • Translate research-grade or prototype data science code into production-ready implementations • production-ready implementations • Work with large datasets and improve efficiency related to memory usage, runtime, and compute cost • Contribute to and maintain production data pipelines and workflows. • production data pipelines and workflows. • Collaborate closely with other data scientists to preserve model intent, correctness, and assumptions while improving implementation quality • model intent, correctness, and assumptions • Debug and resolve issues in production or near-production data science workflows • Improve robustness, monitoring, and maintainability of deployed models and pipelines • Support iterative model improvements and system evolution as business needs change
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