Team: Engineering
City: London
Country: United Kingdom
Veeva Systems is a mission-driven organization and pioneer in industry cloud, helping life sciences companies bring therapies to patients faster. As one of the fastest-growing SaaS companies in history, we surpassed $2B in revenue in our last fiscal year with extensive growth potential ahead.
At the heart of Veeva are our values: Do the Right Thing, Customer Success, Employee Success, and Speed. We’re not just any public company – we made history in 2021 by becoming a public benefit corporation (PBC), legally bound to balancing the interests of customers, employees, society, and investors.
As a Work Anywhere company, we support your flexibility to work from home or in the office, so you can thrive in your ideal environment.
Join us in transforming the life sciences industry, committed to making a positive impact on its customers, employees, and communities.

REQUIREMENTS

• 5+ years of developing data & analytics products like sentiment analyzers, translation engines, summarizers, or other language services
• Expert in Machine Learning, Modeling, and development with Python
• Expert in MLOps with at least one platform, e.g.: MLflow, Kubeflow, or end-to-end automation with SageMaker services
• Ability to mentor others and work independently
• Strong communication skills

THE ROLE

Turn your data science skills into tangible impact! Develop innovative services that make a difference. We’re a rapidly growing company looking for teammates to solve complex problems and bring solutions to life in large-scale software products.
The Veeva Link engineering team seeks Data Scientists and Machine Learning Engineers to participate in all aspects of building and operating AI language features that help our global clients bring products to market faster.

WHAT YOU’LL DO

• Work within a cross-functional application team to build scalable language services in the range of NLP, NLU, and NLG for live SaaS products
• Help develop and groom an experiment backlog
• Build models that solve real-world problems
• Optimize models for production throughput and uptime requirements
• Automate deployments, testing, and monitoring (MLOps)