Principal Engineer - Data Science

Pull Logic is transforming the retail supply chain with a revolutionary availability-oriented paradigm that directly links customer preferences with real-time inventory. Backed by cutting-edge research from  Georgia Tech’s School of Industrial and Systems Engineering , our AI-driven SaaS platform helps retailers and brands reduce lost sales, improve profitability, and elevate customer satisfaction.

We’re looking for a  Principal Engineer – Data Science / Machine Learning  to join our core technology team. This is a unique opportunity to shape the foundation of Pull Logic’s enterprise-grade Retail Tech products and work on solutions that have a direct impact on how modern retailers operate.

What You’ll Do Own the end-to-end development  of data science and machine learning solutions from problem definition and data exploration to production deployment and performance monitoring following clean coding practices and  SOLID principles .

Architect and implement scalable data and ML systems , ensuring reliability, reproducibility, and low-latency model serving across Pull Logic’s platform.

Design and automate ML pipelines  (data ingestion, feature engineering, training, validation, deployment) using modern MLOps tools and frameworks while maintaining adherence to  object-oriented design and SOLID software architecture .

Prototype rapidly , test hypotheses, and iterate based on performance metrics and user feedback.

Lead technical design reviews  and set best practices for data science engineering, experimentation, and ML deployment.

Contribute to the core data architecture , including data modeling, feature stores, and real-time data flows powering decision intelligence.

Mentor and guide other engineers and data scientists , fostering a culture of experimentation, continuous learning, and delivery excellence.

Partner with leadership to  shape the long-term AI and data strategy , ensuring our ML systems scale with product and customer growth.

What We’re Looking For 7 +  years  of experience building and shipping data-driven products or Data Science-powered applications in fast-paced, high-growth environments

3+ years  of hands-on MLOps experience, owning the full lifecycle — from experimentation and feature engineering to production deployment and continuous model improvement

Strong command of  Python  and core data science libraries (Pandas, NumPy etc.), with the ability to write  clean, modular, and testable code that adheres to SOLID principles

Deep understanding of software design patterns, APIs, and scalable data architectures for integrating ML into real-world systems

Proven ability to design, train, and deploy Data Science models and optimization algorithms that directly drive product features or business outcomes

Comfortable working across the entire data stack from pipelines and feature stores to inference APIs and monitoring

Solid grounding in statistics, optimization, and operations research, with a practical mindset for applying theory to solve business problems

Thrive in ambiguity, able to prototype quickly, validate ideas with data, and scale successful solutions

Excellent communicator who can collaborate with product, engineering, and leadership teams to align technical direction with business priorities

MS / MTech or PhD  in  Computer Science, Data Science, Industrial Engineering, or a related technical field  (preferred but not required for exceptional candidates)

Why Pull Logic? Be part of a mission-driven company redefining supply chain intelligence for the retail world.

Work on cutting-edge technology with real-world impact and high visibility.

Collaborate with researchers and engineers from  Georgia Tech , one of the top engineering schools in the world.

Grow with a dynamic, fast-paced team and play a key role in building the future of our platform.

Performance-based ESOP opportunities  for exceptional contributors who help drive Pull Logic’s success and growth.

Ready to Build What’s Next?

Send your resume to careers@pulllogic.com   and let’s start the conversation.