Senior Data Scientist II - AI Sub-Agents (Remote)

Logoslabs; San Francisco (CA); Remote; USD 180,000 - 240,000; Full time

Job summary

Smartsheet is seeking an experienced Senior Data Scientist II to build ML models and AI sub-agents that power growth, monetization, and retention across the customer lifecycle. You will design end-to-end solutions, translate complex modeling into actionable recommendations, and ship production-grade systems for millions of users.

You'll collaborate with Product and Engineering, own modeling problems from framing to deployment, and mentor peers on the team while upholding responsible AI practices

Qualifications

• Bachelor’s degree in a quantitative field required
• 8+ years of experience (or 10+ with advanced degree) preferred
• Strong ML expertise across traditional ML and deep learning
• Experience with LLMs, agent-based systems, and production deployment
• Proficiency in SQL and Python; experience with Spark/Databricks
• Ability to work cross-functionally and communicate analyses clearly

Responsibilities

• Design and ship AI sub-agents that act across the customer lifecycle
• Build predictive and prescriptive models such as churn risk, growth, and account health
• Develop data foundations and knowledge layers for sub-agents
• Design tools, retrieval, grounding strategies, and decision logic for sub-agents
• Create evaluation harnesses and monitor production performance
• Define metrics and experimentation strategy for sub-agent rollouts
• Collaborate with Product, Engineering, and Applied AI teams from framing to deployment
• Mentor other data scientists and promote a data-driven culture

Job description

Smartsheet is seeking an experienced Senior Data Scientist II to build ML models and AI sub-agents that power growth, monetization, and retention across the customer lifecycle. You will design end-to-end solutions, translate complex modeling into actionable recommendations, and ship production-grade systems for millions of users.

You'll collaborate with Product and Engineering, own modeling problems from framing to deployment, and mentor peers on the team while upholding responsible AI practices