We are seeking a Director of Predictive Marketing Analytics with 9–12 years of experience to lead, scale, and elevate our predictive marketing analytics capabilities. This leader will guide a team to deliver high-impact insights, predictive models, and frameworks that shape marketing strategy, optimize investment, and directly influence growth.

This role requires someone who thrives where analytics meets business impact; partnering deeply with marketing and business leaders to understand their challenges and decisions, then building and delivering data-driven solutions that drive results. The right candidate balances technical excellence with business acumen and inspires teams to pursue clarity, simplicity, and innovation in everything they do.

What You’ll Do

Core Responsibilities:

• Lead predictive modeling projects such as lead scoring, customer lifetime value (LTV), opportunity propensity, and other advanced analytics use cases.

• Develop and implement advanced attribution models (multi-touch, algorithmic, or data-driven) to measure true campaign effectiveness.

• Lead predictive modeling efforts including lead scoring, lifetime value (LTV) modeling, marketing attribution.

• Apply machine learning and statistical techniques to drive segmentation, personalization, and optimization of audience targeting.

• Develop, validate, and deploy statistical and machine learning models using R, Python, or STATA to drive marketing and business growth.

• Maintain a deep knowledge of industry trends and best practices to ensure analytics tools and models reflect cutting-edge approaches.

• Actively communicate the impact of predictive analytics to non-technical partners through clear insights and recommendations.

Broader Leadership Responsibilities:

• Set the Vision: Define and execute the marketing data science strategy, aligning data-driven insights with the organization’s growth priorities.

• Deep Business Partnership: Work closely with stakeholders across marketing, product, and sales to identify the most impactful questions to answer—focusing on business outcomes rather than academic exercises.

• Deliver Impactful Insights: Guide the development of models, forecasting, attribution frameworks, and experimental designs that directly optimize how we acquire, engage, and retain customers.

• Drive Accountability & Ownership: Ensure your team takes end-to-end ownership of solutions; from scoping and modeling through delivery, communication, and adoption.

• Focus on Simplicity & Clarity: Ensure outputs are easy for business partners to understand and act on, avoiding unnecessary complexity in tools, data, and communication.

• Raise Standards, Together: Set a high bar for quality and innovation while embracing iteration, feedback, and even failure as part of the learning journey.

• Champion Continuous Improvement: Lead the team with a forward-looking mindset—more focused on where we’re headed and how we’re getting better, rather than where we stand today.

• Feedback & Culture: Encourage a culture of open, constructive feedback where every voice is valued in making the team stronger.

What We’re Looking For

• Experience: 9–12 years in data science, marketing analytics, or a related field, with at least 3–5 years leading teams.

• Technical Expertise:

• Strong grasp of statistical modeling, predictive analytics, and experiment design

• Expertise in SQL, with proficiency in Python, R, or STATA for advanced analytics and machine learning

• Advanced experience in marketing-specific predictive methods: lead scoring, attribution modeling, and customer LTV

• Familiarity with modern data stack tools (e.g., Snowflake, dbt, Looker) and marketing data sources (MarTech, CRM, digital platforms)

• Business Acumen: Proven success translating complex data into clear, actionable insights for marketing and business leaders, with emphasis on impact over volume.

• Leadership: Track record of building, mentoring, and leading high-performing teams in fast-paced environments.

• Mindset & Values:

• Ownership-driven: you measure success by business outcomes, not just technical deliverables

• Committed to simplicity and clarity in communication

• Holds high standards while embracing continuous learning and iteration

• Proactive in building strong stakeholder relationships and deeply understanding their challenges

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