Sr Manager Applied Intelligence

Academy Sports + Outdoors•Katy, TX

2d•Onsite

About The Position

In this role, you shape scalable, reusable, production-grade data science and artificial intelligence capabilities that support customer, marketing, and omnichannel work. Your leadership helps teams move faster with shared data, feature, modeling, MLOps, and model-lifecycle patterns instead of rebuilding solutions for each need. By embedding quality, fairness, trust, compliance, and practical governance into the work, you help create analytics and machine learning solutions that can be used safely and effectively at scale. Working across the customer data domain, you translate business needs into technical patterns aligned with enterprise platforms and standards. You 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. Your scope also includes leading Adobe Analytics and Quantum Metrics data capture, governing customer-facing data domains such as tag management, Bridg, and clean room capabilities, and helping define secure, documented customer data flows.

Requirements

Bachelor's degree in engineering, Statistics, Data Science, or Computer Sciences
8+ years in data science, advanced analytics, applied machine learning, or related fields
4+ years leading teams or major technical initiatives, including people management or matrixed leadership
3+ years in B2C, retail, e-commerce, marketing analytics, or customer-facing analytics domains
3+ years building, deploying, or enabling production-grade analytics or ML solutions
Strong engineering mindset with modular code, reproducibility, and production-grade analytics or ML systems
Background working with digital analytics, customer data platforms, or experimentation data such as Adobe, web or app analytics, or similar ecosystems
Proven ability to influence across matrixed organizations without direct ownership
Background 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 while influencing across a matrixed organization
Focus on scale and reuse rather than personal ownership
Acceptable level of hearing and vision to perform job duties
Ability to adhere to company policies and professional standards

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
A team-first attitude; while your core duties are your priority, you're happy to step in wherever you're needed
Energy, ideas, and a love for sports and the outdoors

Responsibilities

Shape scalable, reusable, production-grade data science and artificial intelligence capabilities.
Lead teams to move faster with shared data, feature, modeling, MLOps, and model-lifecycle patterns.
Embed quality, fairness, trust, compliance, and practical governance into the work.
Create analytics and machine learning solutions that can be used safely and effectively at scale.
Translate 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 such as 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 to influence customer data models, shared datasets, feature reuse, data access, and consumption standards.
Support domain-aligned data scientists, analysts, and engineers.
Build trust with both business and technical stakeholders.
Define clear standards, use evidence-based influence, and allow teams to execute independently while keeping quality, reuse, and downstream usability in focus.
Enable technology, manage data collection, ensure quality assurance and control, influence others, build processes, and manage security.