Director-Advanced Analytics

About the role

The Director, Advanced Analytics sits at the forefront of AT&T's transformation into a truly evidence‑driven organization. This role is responsible for uncovering the causal truth behind business performance, determining what actions create incremental impact, which investments drive growth, and where opportunities exist to accelerate customer and financial outcomes. As the organization’s leader in causal measurement and experimentation, the Director will pioneer the development of advanced causal models, experimentation frameworks, and analytical systems that uncover the true drivers of business performance. By integrating Marketing Mix Models (MMM), causal inference, experimentation, predictive analytics, and AI‑enabled decision intelligence, this leader will establish the trusted source of causal truth for AT&T, revealing how media investments, offer strategies, sponsorships, and growth initiatives generate incremental business value and enabling leaders to make faster, smarter, and more confident investment decisions.

The Director owns the end‑to‑end measurement ecosystem, from defining strategic business questions and designing rigorous experiments to developing advanced analytical solutions, validating insights, and driving organizational adoption. Success requires challenging assumptions, uncovering hidden drivers of performance, and translating complex analytical findings into simple, decision‑grade recommendations that influence strategy and investment decisions at the highest levels of the organization.

About the Marketing & Growth Organization: AT&T’s marketing engine, responsible for driving sustainable growth by deeply understanding and championing customers, captivating them with compelling products, services, and experiences, and building a culturally relevant and beloved brand.

Key responsibilities

• Establish AT&T’s Marketing Causal Truths.
• Develop AT&T’s causal measurement architecture by integrating causal inference, experimentation, advanced econometrics, MMM, and AI‑enabled analytics into a unified intelligence platform.
• Build scalable causal models that quantify incremental impact, identify business drivers, and predict outcomes across media, offers, sponsorships, and growth initiatives.
• Embed causal evidence into forecasting, optimization, and investment planning to improve resource allocation and business performance.
• Advance measurement science capabilities through the application of AI, machine learning, and next‑generation causal modeling techniques.
• Lead Marketing’s experimentation ecosystem, establishing governance, standards, and frameworks for incrementality measurement, causal validation, and Test & Learn programs.
• Design and oversee market‑moving experiments including geo‑tests, holdouts, quasi‑experimental methods, synthetic controls, and difference‑in‑differences to validate investment effectiveness.
• Develop experimentation roadmaps across media, AI‑enabled search, sponsorships, targeting, influencers, messaging, and emerging growth channels.
• Continuously calibrate MMM and predictive models using causal evidence to improve forecast accuracy and investment recommendations.
• Accelerate organizational learning by transforming experimental results into repeatable business practices and scalable growth strategies.
• Translate complex causal insights into decision‑ready recommendations that optimize marketing investments and accelerate customer and financial growth.
• Influence strategic decisions through evidence‑based storytelling, connecting measurement outcomes to business impact, investment tradeoffs, and growth opportunities.
• Build stakeholder confidence in measurement outcomes through rigorous validation, methodological transparency, and consistent delivery of trusted insights.
• Lead and develop a high‑performing team of causal measurement experts, advancing capabilities in experimentation, causal models, AI‑enabled analytics, and marketing science.
• Establish causal measurement as a competitive advantage, enabling faster, smarter, and more confident business decisions.

Requirements

• Deep expertise in causal measurement, developing and applying advanced causal methodologies including RCTs, geo experimentation, synthetic controls, difference‑in‑differences, regression discontinuity, Bayesian approaches, and causal machine learning.
• Ability to translate causal assumptions into graphical and structural frameworks such as Directed Acyclic Graphs (DAGs) and Structural Causal Models (SCMs) to inform research design, identify bias, and strengthen causal interpretation.
• Technical leader with strong scientific rigor, experienced in developing, validating, and operationalizing causal frameworks that combine market tests, observational data, media experiments, MMM, forecasting systems, and optimization models into trusted business recommendations.
• Thought leader in measuring innovation, continuously advancing the application of causal AI, machine learning, experimental design, and emerging measurement methodologies to improve how the organization learns, predicts outcomes, and allocates resources.
• Exceptional storyteller and executive influencer, translating complex analytical findings into compelling, decision‑grade recommendations that drive action at the highest levels of the organization.
• Builder of high‑performing teams, fostering a culture of scientific curiosity, technical excellence, continuous learning, and measurable business impact while developing the next generation of measurement and analytics leaders.
• Strategic partner and change agent, capable of influencing across highly matrixed organizations, aligning diverse stakeholders around evidence‑based decisions, and building trust in measurement outcomes through methodological rigor and transparency.

Pay

Annual salary range: $210,600 – $316,000 USD. Individual starting salary within this range may depend on geography, experience, expertise, and education/training.

Benefits

• Medical, dental, and vision coverage
• 401(k) plan
• Tuition reimbursement program
• Paid parental leave
• Paid caregiver leave
• Additional sick leave beyond statutory requirements (unprotected)
• Adoption reimbursement
• Disability benefits (short‑term and long‑term)
• Life and accidental death insurance
• Supplemental benefit programs: critical illness, accident, hospital indemnity, group legal
• Employee Assistance Programs (EAP)
• Extensive employee wellness programs
• Employee discounts up to 50% on eligible AT&T mobility plans and accessories, AT&T internet (including fiber where available), and AT&T phone
• Long‑term grants and deferred compensation
• Paid time off and holidays (at least 28 days of vacation per year and 9 company‑designated holidays, based on date of hire)

Schedule

• Location: Dallas, Texas (office presence required minimum of 5 days per week)