At Viaduct, we are developing an end-to-end AI-powered machine learning platform to empower industry to build safer, more intelligent, and reliable assets. Our platform increases the accessibility and actionability of connected asset data for industry, their partners and end-customers. We are a diverse team motivated to solve the hardest problems in the asset performance management sphere. Who You Are You are motivated by real-world problems and enjoy working with stakeholders to solve their biggest challenges. You are excited by opportunities to deliver value by turning data science into actionable business and technology insights. Expected Skills - Strong machine learning and statistical analysis skills - Comfort working side-by-side with customers - Strong presentation skills communicating technical concepts to non-technical audiences - Familiarity with working on datasets that don't fit within a single machine - Self-motivated and able to work independently - Excellent spoken and written communication skills - Minimum of advanced degree (M.S.) in a data science-related field and 2+ years of experience in a related field - Proficiency in Python and SQL - Hands-on experience with ML libraries (scikit-learn, xgboost, MLlib, etc.) - Familiarity with deep learning libraries (Tensorflow, Keras, PyTorch, etc.) - Working knowledge of distributed/cloud compute platforms (AWS, Hive, Clickhouse, Argo-workflows, etc.) - Developing agentic workflows - Experience deploying and maintaining data pipelines and DS-backed product features About the Role Day 5 - Learn about Viaduct’s history and mission - Get to know every team member - Set up your development environment - Understand Viaduct’s data pipelines and data science workflow - Learn about our current partners, their biggest needs, and the gaps they're looking for us to fill Day 30 - Present your first deliverables internally - Scope-out and propose improvements to our products - Deploy improvements to our ML/Agentic pipelines - Present your work at our weekly team meetings Day 90 - Be a thought leader on the data science team - Deploy world-class DS-based products and services - Collaborate with engineers on improvements to data science tooling - Present your models and relevant metrics to stakeholders Security and Privacy Responsibilities - Follow our policy and procedure documents related to security and privacy - Follow the guidelines in the Employee Handbook - Participate in new hire and annual training for security and privacy - Treat data security and privacy as one of your primary job responsibilities - Report Security Incidents you discover as bugs - Get approval from the Security Team before adding new 3rd party software to our codebase - Explicitly consider security implications when doing PR reviews

Data Scientist

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