Senior Manager, Data Science and Machine Learning
Lead a 12-person data science and ML team at Squarespace, setting technical and strategic direction for predictive modeling, causal inference, and AI evaluation systems that power marketing measurement, growth, and risk/credit modeling. Hybrid NYC role (3 days/week in office) reporting to the VP of Data.
- Compensation
- $223K–$358.8K / yr
- Location
- New York City
- Work setup
- Hybrid
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Data science
- Posted
Skills
Must have
- 12+ years in data science or machine learning, including leading technical teams through complex, high-stakes projects
- 5+ years managing managers, with ability to develop senior ICs and team leads simultaneously
- Hands-on experience building or overseeing production predictive models, causal inference frameworks, or AI evaluation systems
- Skill at aligning product, engineering, and business partners around a shared roadmap and explaining statistical/technical trade-offs to executives
- Experience scaling a data science or ML function, including team structure, hiring, and technical standards
Nice to have
- Experience building or overseeing LLM evaluation harnesses, including golden sets and LLM-as-judge methods
- Familiarity with prompt and model lifecycle management in production
- Experience designing human-in-the-loop review for AI-driven features
- Background in marketing science, experimentation, or credit/risk modeling
Full description
Senior Manager, Data Science and Machine Learning
New York, NY
Squarespace is hiring a Senior Manager, Data Science and Machine Learning to lead the team behind our predictive models, causal inference frameworks, and AI evaluation systems. These are the tools that inform how we acquire customers, keep them, and serve them at scale. Reporting to the VP of Data, you’ll lead a team of 12 data scientists and ML engineers doing work that is directly tied to how Squarespace leverages AI and data-driven capabilities. You will work closely with product, engineering, and analytics partners across the organization to introduce new ways to leverage data in our products and decision systems, for customers and internal use. This is an opportunity to step into a critical senior leadership seat and set the direction for how machine learning and data science are practiced at Squarespace.
This is a hybrid role working from our NYC office 3 days per week. You will report to the VP, Data.
You'll Get To...
- Lead and develop a team of 12, including three direct reports who manage their own teams
- Set the technical and strategic direction for predictive modeling, causal inference, and AI evaluation across Squarespace's product and business priorities
- Apply data science to Squarespace’s biggest growth levers: marketing measurement, sales growth, product optimization, customer support allocation, and risk and credit modeling
- Partner with product, engineering, and business leaders to translate complex statistical and ML work into decisions and systems that drive customer acquisition, retention, engagement, and growth across our global products.
- Build the systems, processes, and team culture that raise the quality and speed of data science delivery across the organization
- Evolve the team's ML infrastructure and tooling as AI capabilities at Squarespace grow
Who We're Looking For
- 12+ years in data science or machine learning, including experience leading technical teams through complex, high-stakes projects
- 5+ years managing managers, with demonstrated ability to develop senior individual contributors and team leads simultaneously
- Hands-on experience building or overseeing production predictive models, causal inference frameworks, or AI evaluation systems
- Skill at aligning product, engineering, and business partners around a shared roadmap and navigating trade-offs clearly, especially in explaining statistical or technical trade-offs to executives without losing the nuance
- Experience scaling a data science or ML function, including team structure, hiring, and technical standards