DataOps Engineer

ODDITY is a consumer tech company that builds and scales digital-first brands to disrupt the offline-dominated beauty and wellness industries. The company serves over 40 million users with its AI-driven online platform, deploying data science to identify consumer needs, and developing solutions in the form of beauty and wellness products. ODDITY owns IL MAKIAGE and SpoiledChild.

ODDITY LABS is our cutting-edge biotechnology R&D center, powering science-backed product innovation through the discovery and development of new molecules, probiotics, peptides, and other biological modalities. Unlike pharmaceuticals, ingredient innovation in the beauty and wellness industry has remained stagnant for decades. We are deploying patented, proprietary technologies and capabilities, including AI-based molecule discovery and advanced phenotypic databases, to understand the biological mechanisms that drive cellular behavior and develop uniquely efficacious products for beauty and wellness applications. Innovations from the ODDITY LABS will power game-changing products through ODDITY’s current brands, as well as future brands.

ODDITY operates with business headquarters in New York City, an R&D center in Tel Aviv, Israel, and a biotechnology lab in Boston. Our culture is fast-paced, innovative, agile, and offers every team member the opportunity to drive a big impact.

THE ROLE

We are seeking a DataOps Engineer for the Data Platform for ODDITY LABS. In this role, you will:

• Build and maintain large-scale data pipelines that ingest, process, and operationalize hundreds of GB of experimental data each week to support histology screening workflows and help make scientific data-based decisions.
• Partner closely with scientists and cross-functional stakeholders to design and deploy Python-based tools, data systems, and internal products that accelerate discovery workflows, improve lab decision-making, and support compound search, operational efficiency, and intelligent automation.
• Develop scalable systems across structured and unstructured data, including data models, databases, and cloud infrastructure, and deploy out-of-the-box and fine-tuned machine learning models in AWS SageMaker and related services.
• Take ownership from architecture through deployment, help establish best practices in data platform engineering and software quality, and continuously identify new technologies and approaches that can create meaningful advantage for ODDITY LABS.

WHO YOU ARE

• You have 3–5 years of experience in Python-based software development, data engineering, data operations, or data science, with a focus on backend, platform, or data-intensive systems. You have strong experience building production-grade data pipelines, designing and optimizing databases and data models, and working across both structured and unstructured data.
• You are hands-on with AWS and cloud-native infrastructure, and ideally have experience deploying and managing machine learning models in SageMaker or similar environments. You write clean, maintainable, scalable code and are comfortable operating in fast-paced environments with high data volume and evolving business needs.
• You are excited to work closely with scientists and stakeholders and are deeply motivated by building tools that help others move faster and do better work. You are customer obsessed, proactive, and highly accountable. You communicate clearly, thrive in ambiguity, learn quickly, and take pride in owning everything you touch from problem definition through execution.
• You have a strong work ethic, a high quality bar, and a willingness to do whatever it takes to build exceptional products as part of a highly competitive, high-performing team.

How to Apply

Don't just send a resume. Tell us about a time you identified a problem in a workflow and built the solution yourself. Show us your "vibes" - links to GitHub, personal projects, or products you’ve shipped are highly encouraged. Tell me what your favorite tv series, band, or song was in fall of 2009 to showcase how detail oriented you are since you read this whole description :D