What we’re looking for
This is a highly visible, strategic, and impactful role, reporting the VP of Engineering for SurveyMonkey Core Engineering. As the Senior Director of AI, you will be a critical leader in defining the future of SurveyMonkey's products and services. You will not just manage the Machine Learning and Data Science teams, but help drive the fundamental shift in how our customers gather, analyze, and act on feedback. This requires both strong strategic thinking, and solid experience building models and deploying AI/ML systems end-to-end (from data to production). The successful candidate will make a material contribution to the company's success by providing thought leadership around how to leverage AI technology in the SurveyMonkey product in an intuitive and natural way.
What you’ll be working on
Strategic Leadership & Product Alignment
- Strategic Roadmapping: Define and execute the long-term data science strategy, ensuring alignment with SurveyMonkey’s overarching business objectives and product vision.
- Product-to-Data Science Translation: Act as the primary liaison between Product Management and the Data Science team, expertly mapping ambitious SurveyMonkey product requirements (e.g., automated thematic analysis, predictive modeling, intelligent survey generation) into clear, technically feasible data science projects.
- AI-First Transformation: Support the charge in migrating SurveyMonkey from a traditional SaaS product to an AI-first platform, embedding advanced data science and LLM capabilities deeply within the product experience.
Technical Expertise & Innovation
- LLM and NLP Integration: Drive the strategy for integrating and leveraging Large Language Models (LLMs) and advanced Natural Language Processing (NLP) techniques to enhance our text analytics, sentiment analysis, and generative features within the product.
- Familiarity with MLOps / LLMOps practices: Experience developing and deploying a Machine Learning Platform. Experience with Agentic AI Architecture, deployment and execution. Familiarity with modern MLOps tools and platforms (e.g., AWS SageMaker, GCP Vertex AI, MLflow).
- Technical Direction: Set the technical standards for model development, deployment, monitoring, and maintenance. Ensure the team utilizes best-in-class data science models, tools, and platforms for scalable production systems.
- Ownership of model lifecycle: experimentation → deployment → monitoring → iteration
- Stay Current: Actively research, evaluate, and evangelize the latest developments in machine learning, deep learning, MLOps, and data science methodologies, ensuring the team's capabilities remain cutting-edge.
Team Management & Development
- Management and Motivation: Lead, mentor, and inspire a high-performing team of data scientists and machine learning engineers. Foster a culture of innovation, rigor, collaboration, and continuous learning.
- Recruitment and Growth: Recruit top-tier data science talent, manage performance, and define career progression paths to ensure the ongoing growth and retention of team members.
We’d love to hear from people with
- Experience: 10+ years of progressive experience in data science, including significant experience with AI/ML platforms, and at least 5 years in a Director level leadership role managing and scaling data science or machine learning teams in a commercial software environment.
- Experience building or leading AI/ML platforms: (feature stores, model serving, training pipelines) in a Cloud Environment like AWS
- Deep Technical Acumen: Extensive practical experience with and deep theoretical understanding of machine learning algorithms, statistical modeling, and experimental design (A/B testing).
- NLP and LLM Proficiency (Critical): Demonstrated experience in applying, fine-tuning, and integrating Large Language Models (LLMs) and advanced Natural Language Processing techniques into production systems, especially for tasks related to text classification, summarization, and generation.
- Product Thinking: Exceptional ability to translate ambiguous or high-level product needs into concrete, defensible data science problems and solutions.
- Communication: Excellent written and verbal communication skills, with the ability to articulate complex technical concepts to non-technical stakeholders (e.g., Executives, Product Managers, Marketing).
- Education: Master’s degree or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field.