Full-Stack Data Scientist

Nimbus

2d•Remote

About The Position

As a Full-Stack Data Scientist at AdsByNimbus, you will occupy a critical hybrid role that bridges the gap between high-velocity backend software engineering and advanced data science. You won't just build machine learning models in a vacuum; you will actively architect, scale, and maintain the cloud-based AWS pipelines that power them. Operating within our lean, highly capable engineering team, you will be the core technical force translating complex analytical insights into production-ready decisioning frameworks that directly impact real-time ad delivery and platform optimization. This role is designed for a pragmatic technical builder who thrives in extremely high-volume data environments. You will take deep ownership of our analytics lifecycle—from designing robust experimentation frameworks for dynamic auction yield optimization to deploying and monitoring sophisticated AI agents and forecasting models. If you are a software engineer who has mastered data science, or a data scientist who writes clean, production-grade code and is committed to rigorous engineering best practices, you will have the autonomy to significantly shape the future of our data infrastructure.

Requirements

Strong data analytics capabilities with a proven track record of handling high-volume, Big Data environments.
Deep understanding of Machine Learning principles and application.
Strong understanding of AI, specifically regarding agent deployment, maintenance, and practical usage.
Hands-on experience architecting and working within cloud environments, specifically AWS.
Comfortable reading and writing production-level code. Proficiency in Python is required; experience with backend languages like Go is a significant plus.
A strong commitment to engineering best practices, including rigorous logging, documentation, and debugging.
Experience building optimization or decisioning systems in extremely high volume environments.
Excellent teamwork and communication skills, with the ability to articulate complex technical concepts to a lean, highly capable team.
A self-starter mindset with the ability to adapt to new technologies and learn quickly in a fast-paced ad-tech landscape.

Nice To Haves

Experience with backend languages like Go is a significant plus.

Responsibilities

Lead Data Analytics & Modeling: Drive data initiatives using both traditional machine learning and emergent AI technologies. Focus on pragmatic, non-generic applications that empower human decision-making and optimize our platform.
Data Pipeline Engineering: Work closely with the core engineering team to design, build, and maintain scalable data pipelines that support ML tooling, analytics, and high-velocity ad delivery systems.
Cross-Functional Collaboration: Leverage your software engineering proficiency to translate data science concepts into production-ready architecture, ensuring seamless integration between data models and backend systems.
Experimentation & Optimization: Design, evaluate, and operationalize experimentation frameworks for auction, pricing, and yield optimization. Building scalable methods to measure impact, validate model performance, and improve revenue driving decision systems.
Model Production & Monitoring: Productionize forecasting and optimization models by building backtesting, monitoring, and guardrail systems that ensure outputs are reliable, explainable, and safe to deploy in high output and delivery environments.