About the role

Role Overview

Join Cordial’s data science team as a Data Scientist (Production Engineering). You will help operationalize, optimize, and scale data science models and systems that are already in production or moving into production.

Key Missions

Optimize existing data science models and systems for performance, scalability, and reliability
Translate research/prototype code into production-ready implementations
Collaborate with data scientists, engineering, and product teams to preserve model intent, correctness, and assumptions while improving implementation quality
Resolve issues in production workflows and improve implementation quality

Responsibilities

Work closely with teams to improve implementation quality and reliability of model deployments
Ramp up quickly in existing codebases and take ownership in a small, fast-moving environment
Contribute to production-grade data science / analytics systems

Requirements

Solid data science fundamentals (statistics and modeling concepts)
3+ years working with real-world, production data in a data science / applied ML / analytics role
Ability to work independently and ramp up quickly in an existing codebase
Experience in small, fast-moving teams with ownership and autonomy
Strong Python skills and ability to write maintainable, production-quality code
Demonstrated experience contributing to production data science or analytics systems

Cloud & Data Engineering Requirements

Cloud expertise, especially AWS services (e.g., Glue, Athena, ECS, S3)
Experience with data pipelines and orchestration (e.g., Dagster, Airflow)
Big data/distributed computing experience for enterprise-scale event datasets
Experience with large datasets and performance-sensitive workflows
Hands-on experience with modern data warehouses (e.g., Snowflake, BigQuery), including:
- Query optimization
- Clustering strategies
- Cost management

Nice-to-have / Context

The posting includes a code word: #becordial

Location & Work Setup

Full remote

About Cordial

Cordial is a company building data-driven products and platforms, with a focus on operationalizing and scaling data science solutions. It employs a data science team that works closely with engineering and product to improve the quality and reliability of models and production workflows. The role emphasizes production engineering for applied ML systems.

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