Data Scientist - Production Engineering

ABOUT CORDIAL We founded Cordial in 2014 on the belief that there should be more humanity and empathy in marketing—both in how brands communicate with their customers and in how technology companies work with brands. We built our company and platform purposefully, driven by a desire to inspire more thoughtful communication and to create experiences that feel more personal and human—for consumers, for the people at the companies we work with, and for Cordial employees. L.

Bean and Forbes rely on Cordial to drive revenue growth by sending a better message. We chose the name Cordial to symbolize how we empower our clients to communicate with their customers, as well as how we do business: with transparency, collaboration, and trust. We're building a passionate team of individuals willing to learn, grow, and be thoughtfully challenged on a daily basis to continuously improve our product, company, and culture every single day.

OUR VALUES Communicate better than the rest Own it, every time Solve client problems tenaciously Make Waves Role Overview We are looking to add a Data Scientist – Production Engineering to our data science team. This role focuses on operationalizing, optimizing, and scaling data science models and systems that are already in production or transitioning into production. Our data science work extends beyond traditional machine learning and includes statistical models, optimization logic, heuristics, rules-based systems, ML and LLM models , all operating on large, real-world datasets.

This role is designed to strengthen the implementation and performance side of our data science stack while working closely with data scientists, engineering and product teams. This role requires someone who can ramp up quickly , work independently, and contribute meaningfully without extensive onboarding.

Key Responsibilities

Optimize existing data science models and systems for performance, scalability, and reliability Translate research-grade or prototype data science code into 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.

Qualifications

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 Demonstrated experience contributing to production data science or analytics systems , not only exploratory or academic projects Strong programming skills in Python and experience writing maintainable, production-quality code Experience working with large datasets and performance-sensitive workflows The code word is #becordial Prior experience with data pipelines and orchestration frameworks (e.g., Dagster, Airflow, etc.) 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 Experience with big data technologies and distributed computing frameworks for handling enterprise-scale event datasets 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 ra