Sr Manager Applied Intelligence

Academy•Katy, TX

1d•Onsite

About The Position

Shape scalable, reusable, production-grade data science and artificial intelligence capabilities supporting customer, marketing, and omnichannel work. Lead the development and adoption of shared data, feature, modeling, MLOps, and model-lifecycle patterns so teams can work efficiently without rebuilding solutions for each need. Embed quality, fairness, trust, compliance, and practical governance into analytics and machine learning solutions so they can be used safely and effectively at scale. Work across the customer data domain to translate business needs into technical patterns aligned with enterprise platforms and standards. Influence priorities through evidence, design proposals, and proof-of-value work while enabling data scientists, analysts, and engineers to adopt common approaches and reduce reinvention. Lead Adobe Analytics and Quantum Metrics data capture, govern customer-facing data domains such as tag management, Bridg, and clean room capabilities, and help define secure, documented customer data flows. Partner with CIO, Data Products, Engineering, AI COE, IT pod, platform, and governance teams to influence customer data models, shared datasets, feature reuse, data access, and consumption standards. Support domain-aligned data scientists, analysts, and engineers across a matrixed organization, building trust with business and technical stakeholders and keeping quality, reuse, and downstream usability in focus. Regular in-office attendance is required.

Requirements

Bachelor's degree in engineering, Statistics, Data Science, or Computer Sciences.
At least 8 years in data science, advanced analytics, applied machine learning, or related fields.
At least 4 years leading teams or major technical initiatives, including people management or matrixed leadership.
At least 3 years in B2C, retail, e-commerce, marketing analytics, or customer-facing analytics domains.
At least 3 years building, deploying, or enabling production-grade analytics or machine learning solutions.
Strong engineering mindset, including modular code, reproducibility, and production-grade analytics or ML systems.
Experience with digital analytics, customer data platforms, or experimentation data, such as Adobe, web or app analytics, or similar ecosystems.
Ability to influence across matrixed organizations without direct ownership.
Experience partnering with data engineering, platform, and governance teams.
Ability to balance speed to value with long-term scalability.
Systems-thinking approach with a pragmatic bias toward delivery.
Ability to build trust with business and technical stakeholders.
Comfort enabling teams to execute independently and influencing across a matrixed organization.
Focus on scale and reuse rather than personal ownership.
Regular in-office attendance is required.
Acceptable level of hearing and vision to perform job duties.
Ability to adhere to company policies and professional standards.
A team-first attitude and willingness to step in where needed.

Nice To Haves

Master's degree in Analytics or data science preferred.
Retail or B2C experience preferred.
MLOps or analytics platform enablement preferred.
Exposure to customer analytics, personalization, marketing, or e-commerce use cases preferred.
Background operating in early-to-mid maturity data organizations preferred.
Comfort shaping standards without formal authority preferred.

Responsibilities

Shape scalable, reusable, production-grade data science and artificial intelligence capabilities for customer, marketing, and omnichannel work.
Develop and promote shared data, feature, modeling, MLOps, and model-lifecycle patterns.
Embed quality, fairness, trust, compliance, and practical governance into analytics and machine learning solutions.
Translate customer-domain business needs into technical patterns aligned with enterprise platforms and standards.
Influence priorities through evidence, design proposals, and proof-of-value work.
Enable data scientists, analysts, and engineers to adopt common approaches and reduce reinvention.
Lead Adobe Analytics and Quantum Metrics data capture.
Govern customer-facing data domains, including tag management, Bridg, and clean room capabilities.
Help define secure, documented customer data flows.
Partner with CIO, Data Products, Engineering, AI COE, IT pod, platform, and governance teams on customer data models, shared datasets, feature reuse, data access, and consumption standards.
Support domain-aligned data scientists, analysts, and engineers across a matrixed organization.
Build trust with business and technical stakeholders and promote quality, reuse, and downstream usability.