Senior Data Scientist

Workato delivers enterprise infrastructure for the agentic era, redefining iPaaS and helping enterprises unify data, applications, processes, and AI into a single, governed platform. A leader in Enterprise MCP and trusted by 50% of the Fortune 500, Workato’s cloud-native architecture connects every application, data source, and process to power real-time orchestration at scale. With enterprise-grade security and continuous innovation at its core, Workato provides the trusted foundation for organizations to automate with confidence and operationalize AI across the business. To learn more, visit www.workato.com

Why join us?

Ultimately, Workato believes in fostering a flexible, trust-oriented culture that empowers everyone to take full ownership of their roles. We are driven by innovation and looking for team players who want to actively build our company.

But, we also believe in balancing productivity with self-care. That’s why we offer all of our employees a vibrant and dynamic work environment along with a multitude of benefits they can enjoy inside and outside of their work lives.

If this sounds right up your alley, please submit an application. We look forward to getting to know you!

Also, Feel Free To Check Out Why

• Business Insider named us an “enterprise startup to bet your career on”
• Forbes’ Cloud 100 recognized us as one of the top 100 private cloud companies in the world
• Deloitte Tech Fast 500 ranked us as the 17th fastest growing tech company in the Bay Area, and 96th in North America
• Quartz ranked us the #1 best company for remote workers

We are looking for an exceptional Middle / Senior Machine Learning Engineer / Data Scientist to join our growing AI team. In this role, you will design, build, deploy, and improve ML/LLM-powered services and features that power intelligent automation and AI-driven product experiences across the Workato platform. You will work closely with our Engineering, Product, and Design teams to define and track product metrics and evaluation strategies, design customer-facing experiments and dive deep to provide actionable insights.This role is ideal for someone who combines strong ML/LLM intuition, software engineering skills and a practical mindset for shipping reliable, scalable AI systems.

In This Role, You Will Also Be Responsible To

• Build and improve AI services using LLMs and custom machine learning models for production use cases.
• Design, develop, and operate ML/LLM systems end-to-end, from prototyping to deployment and monitoring.
• Write high-quality Python code that is testable, maintainable, and efficient.
• Improve validation, observability, and performance monitoring for ML services (quality, latency, reliability, cost).
• Partner cross-functionally with product managers, platform engineers, and other stakeholders to ship AI-powered product capabilities.
• Evaluate and improve existing implementations by identifying bottlenecks, bugs, and opportunities for optimization.
• Design controlled experiments to test the features for our AI-based products and perform deep analysis from the results to find actionable insights
• Contribute to technical design and code reviews, helping raise engineering quality across the team.
• Experiment and iterate on model behavior, prompting, retrieval, tool use, or orchestration strategies to improve user outcomes.

Qualifications / Experience / Technical Skills

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Statistics, or equivalent practical experience
• 3+ years of experience in Machine Learning Engineering, Data Science, or a similar role.
• Strong Python programming skills.
• Hands-on experience with NLP and/or LLM-based systems.
• Strong understanding of software engineering fundamentals (testing, code quality, debugging, version control).
• Ability to work collaboratively in a fast-moving environment and drive projects with ownership.

• Experience with tool-use agents or workflow-aware AI systems.
• Experience building AI products in enterprise SaaS environments.
• Experience with A/B testing and statistical significance techniques.
• Experience with LLMOps/MLOps tooling and practices (monitoring, evaluation pipelines, model rollout, CI/CD).
• Experience working with modern data warehouses such as Amazon Redshift Snowflake.