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.

AI Innovation

• Stay at the forefront of AI and LLM research and GCP platform capabilities (Vertex, Gemini, BigQuery ML, Glean).
• Quickly separate hype from practical value; pilot and harden innovations that can become repeatable, governed patterns for the wider organization.

Required Experience

Experience

• 8+ years in Data Science / Machine Learning, with 3+ years in a formal leadership role managing high‐impact technical teams.
• Proven track record of taking models and AI solutions into production and delivering measurable business outcomes (revenue, retention, efficiency, or cost).

AI & Modeling Expertise

Hands‐on experience with:

• LLM orchestration and RAG architectures (retrieval over internal data, grounding, prompt chaining).
• Fine‐tuning or adapting foundation models for business‐specific contexts.
• A broad range of statistical and ML methods across classification, regression, time‐series, and uplift/propensity modeling.
• Ability to apply these methods to large, messy real‐world datasets and ship something that works now, not just in theory.
• Deep Google Stack Experience (Required)

Significant, recent experience with Google Cloud Platform, including:

• BigQuery for large‐scale analytics, feature stores, and model inputs/outputs.
• Vertex AI and/or BigQuery ML for model training, deployment, and monitoring.
• Comfortable designing solutions that combine BigQuery + dbt + Vertex/BigQuery ML end‐to‐end.

Technical Skills

• Strong proficiency in Python (and/or R) and SQL; familiarity with common ML frameworks.
• Practical knowledge of MLOps patterns:
• Model versioning, CI/CD, monitoring, retraining policies.
• Integration of predictions and agents into production workflows and tools.

Mindset & Leadership

Strong bias for action:

• You prefer a working prototype that solves 80% of the problem this quarter over a perfect model six months from now.
• Demonstrated ability to:
• Attract, grow, and retain high‐caliber DS/AI talent.
• Thrive in a fast‐paced environment with evolving priorities.
• Drive a data‐ and AI‐informed culture across multiple functions.

Education

• Master’s or PhD in a quantitative field (CS, Statistics, Mathematics, Physics, Engineering) preferred, or equivalent deep industry experience.

If you want to shape an enterprise AI strategy on Google Cloud, lead a hands‐on DS/AI team, and turn BigQuery + Vertex into real competitive advantage, we’d like to talk.