Applied Science / Data Science Leader
Attentive
| Company | Attentive |
| Category | Data & Analytics |
| Location | San Francisco |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 8 Jul 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (greenhouse) |
Description
Attentive® is the AI marketing platform for 1:1 personalization redefining the way brands and people connect. We’re the only marketing platform that combines powerful technology with human expertise to build authentic customer relationships. By unifying SMS, RCS, email, and push notifications, our AI-powered personalization engine delivers bespoke experiences that drive performance, revenue, and loyalty through real-time behavioral insights.
Recognized as the #1 provider in SMS Marketing by G2, Attentive partners with more than 8,000 customers across 70+ industries. Leading global brands like Crate and Barrel, Urban Outfitters, and Carter’s work with us to enable billions of interactions that power tens of billions in revenue for our customers.
With a distributed global workforce and employee hubs in New York City, San Francisco, London, and Sydney, Attentive’s team has been consistently recognized for its performance and culture. We’re proud to be included in Deloitte’s Fast 500 (four years running!), LinkedIn’s Top Startups , Forbes’ Cloud 100 (five years running!), Inc.’s Best Workplaces , and the Human Rights Campaign Foundation's Corporate Equality Index ! About the Role
Our Applied Science / Data Science team is a world-class organization focused on using data, experimentation, machine learning, and AI to shape product strategy and accelerate business growth. We partner closely with Product, Engineering, Marketing, Sales, and Customer Success to build intelligent products, optimize customer experiences, and drive measurable business impact.
This will be a leadership role overseeing our applied science / data science team and you will lead a team of high-performing data scientists responsible for solving some of the company’s most strategic product and business challenges. You will define the vision for applied data science across multiple product areas, develop innovative analytical and machine learning solutions, and partner with senior leaders to influence company strategy. This is a highly visible leadership role that blends people management, technical excellence, and cross-functional influence.
What You’ll Accomplish
Lead, mentor, and grow a team of Applied Scientists / Data Scientists, fostering technical excellence and career development
Define and execute the applied data science roadmap in partnership with Product, Engineering, and executive stakeholders
Drive the development of statistical models, machine learning solutions, experimentation frameworks, and causal inference methodologies to improve product performance and customer outcomes
Establish best practices for experimentation, measurement, forecasting, and decision-making across the organization
Translate ambiguous business problems into scalable analytical and machine learning solutions
Influence product strategy by identifying opportunities through deep analysis of customer behavior, experimentation, and business performance
Partner closely with engineering teams to operationalize models and deploy production-ready solutions
Present insights and recommendations to senior leadership and executive stakeholders to influence strategic decisions
Build a culture of scientific rigor, operational excellence, and continuous innovation across the data science organization
Your Expertise
Master’s or Ph.D. in Statistics, Computer Science, Economics, Operations Research, Mathematics, Machine Learning, or a related quantitative field; Bachelor’s degree with equivalent industry experience also considered
8+ years of experience in Data Science, Machine Learning, Analytics, or related disciplines
3+ years of experience managing and developing high-performing data science teams
Deep expertise in experimentation, causal inference, statistical modeling, predictive modeling, and machine learning
Experience partnering clo
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