Senior Manager, Data Science & AI

The Role

We are moving from traditional analytics to a Google Cloud–centric, AI‐driven organization built on BigQuery, dbt, Vertex AI, and Glean.

As our Senior Manager of Data Science & AI, you will:

• Lead a high‐performing, distributed team of Data Scientists (US & EMEA).
• Own the design and deployment of production‐grade ML models and AI agents on BigQuery + Vertex.
• Be a product‐minded builder who uses AI as a lever for business productivity and automated decision‐making – not as a science project.

You’ll combine deep Google stack expertise with a strong sense of business impact and a bias for shipping.

Core Responsibilities

AI Strategy & Vision

• Define and execute a Data Science & AI roadmap that integrates LLMs, GenAI, and classical ML into core functions (GTM, Product, Finance, Operations).
• Partner with Enterprise Data, IT, and business leaders to prioritize use cases by expected impact, feasibility, and time‐to‐value.
• Lean in on the rapidly changing data + AI space (Vertex, Gemini, Glean, agents) and translate platform evolution into a clear plan for SolarWinds.

Agentic Workflows & Applied LLMs

• Lead the design of agentic workflows and AI copilots that:
• Monitor business KPIs and health signals.
• Perform automated root‐cause exploration over governed data.
• Push proactive, explainable “answers” and recommendations to executives and operators.
• Use LLM orchestration, RAG over BigQuery/dbt models, and Vertex/Gemini to build agents that are grounded, auditable, and safe.

Production ML Excellence (BigQuery + Vertex)

• Own the end‐to‐end lifecycle for predictive models (e.g., churn, propensity, adoption, expansion, forecasting):
• Problem framing, feature design, model selection, evaluation.
• Deployment on Vertex AI / BigQuery ML with robust MLOps.
• Writebacks into BigQuery and integration into Tableau, workflows, or agents.
• Ensure AI outputs are anchored in governed dbt models and BigQuery marts to minimize hallucination and maintain executive trust.

Team Leadership & Ways of Working

• Recruit, mentor, and scale a world‐class DS/AI team; set clear expectations for technical quality and business impact.
• Foster a culture of “high‐velocity shipping”:
• Lightweight experimentation with fast feedback loops.
• Code reviews, reproducibility, and MLOps best practices as the norm.
• Clear measurement of impact and iteration based on results.

Collaborate tightly with:

• Data Engineering & Platform (BigQuery, ingestion, performance/cost).
• Analytics Engineering & BI (semantic layer, dashboards, NLQ).
• Data Governance & Security (policies, access, responsible AI).

Stakeholder Evangelism & Communication

• Act as the internal “how to solve X with AI” consultant:
• Translate ambiguous business problems into tractable DS/AI solutions.
• Explain technical trade‐offs, risks, and constraints in clear language.
• Regularly brief GTM, Finance, Product, and Exec stakeholders on what’s possible now, what’s next, and what’s not worth doing.