Sr. Manager of Analytics and Data Science

Love's Travel Stops & Country Stores•Oklahoma City, OK

5d•Onsite

About The Position

The Senior Manager of Analytics & Data Science leads teams of analytics and data science professionals responsible for delivering enterprise-wide insights, advanced analytics, and scalable solutions that drive strategic decision-making across the organization. This leader partners closely with business and technology stakeholders to identify opportunities, solve complex business challenges, and deliver measurable business impact through data.

Requirements

Bachelor's degree required in a quantitative or technical field (e.g., data science, statistics, mathematics, computer science, economics, engineering, or operations research)
8+ years of progressive experience in data science, advanced analytics, or quantitative modeling, including hands-on delivery of machine learning and statistical models in a production environment
3+ years of direct people-leadership experience managing and developing teams of data scientists and analysts
Demonstrated track record of translating ambiguous business problems into analytical solutions that drive measurable, enterprise-level impact
Experience partnering with executive and cross-functional stakeholders to set strategy and influence decisions without direct authority
Deep proficiency in machine learning, statistical modeling, experimentation/A/B testing, forecasting, and optimization techniques
Strong programming and data fluency with Python and/or R, advanced SQL, and modern data platforms (e.g., Snowflake, Databricks, or comparable cloud data warehouses)
Working knowledge of cloud environments (AWS, Azure, or GCP) and MLOps practices for deploying, monitoring, and maintaining models at scale
Proficiency with BI and visualization tools (e.g., Tableau, Power BI, Sigma) to communicate insight clearly to business audiences
Familiarity with generative AI and large language model applications and their responsible, value-driven use in the enterprise
Proven ability to build, scale, mentor, and retain high-performing technical teams
Strong business acumen with the ability to connect analytical work to financial and operational outcomes
Excellent written and verbal communication skills, including the ability to make complex concepts clear and compelling to senior, non-technical leadership.
Skilled at prioritization, roadmap planning, and resource management across competing demands
Self-directed leader who thrives in a fast-moving, collaborative, and evolving environment

Nice To Haves

Master's or PhD strongly preferred
Retail, fuel, hospitality, or large-scale consumer/operations experience preferred

Responsibilities

Drive Strategic Decision-Making Through Analytics: Partner with leaders across the organization to identify opportunities where analytics can improve business performance and support strategic objectives. Translate complex business questions into actionable analytical approaches and measurable outcomes. Deliver insights and recommendations that influence decision-making at all levels of the organization. Promote the use of data and analytics as strategic assets across the enterprise.
Advanced Analytics & Solution Delivery: Oversees the entire lifecycle of analytics projects, including requirement gathering, development, validation, deployment, and monitoring of advanced analytics, machine learning, and AI-enabled solutions. Ensures analytical solutions are scalable, actionable, and aligned to business objectives while delivering measurable business value. Establishes best practices that drive consistency, quality, and adoption across analytics initiatives. Guides teams in balancing technical rigor with practical business application to maximize organizational impact.
Team Leadership & Development: Build and scale a high-performing data science & analytics organization, defining roles, career paths, and operating norms that attract and retain top analytical talent. Lead a team of data scientists, seen as trusted advisors, known for proactive insights and creating thought leadership. Foster a culture of curiosity, accountability, analytical rigor, experimentation, and continuous improvement. Collaborate cross-functionally with Customer Experience, FP&A, and Data Engineering to deliver comprehensive solutions. Provide coaching, performance management, and career development opportunities that support employee growth and engagement. Establish clear priorities and create an environment that enables teams to perform at their highest level.
Strategic Analytics Roadmap: Define and evolve the enterprise analytics roadmap in alignment with organizational priorities. Evaluate and prioritize initiatives to maximize business impact and resource effectiveness. Balance short-term business needs with long-term capability development. Identify opportunities to expand analytics capabilities and increase organizational value.
Strengthen Enterprise Partnerships: Develop trusted relationships with stakeholders across operations, finance, marketing, merchandising, customer experience, technology, and other business functions. Align analytics investments with enterprise priorities and strategic goals. Facilitate collaboration between analytics, technology, and business teams to maximize impact. Effectively communicate complex analytical concepts to technical and non-technical audiences, including senior leadership.
Data Governance & Quality: Partner with technology and data teams to improve data quality, governance, accessibility, and trustworthiness. Advocate for data standards and best practices that support enterprise analytics. Ensure analytical solutions are built on reliable, well-governed data foundations. Promote confidence and trust in analytical outputs across the organization.
Innovation & Continuous Improvement: Champion the adoption of emerging analytics, automation, and AI-enabled capabilities that improve business outcomes, accelerate insight generation, and enhance team effectiveness. Encourage experimentation and continuous improvement in analytical approaches and methodologies. Partner with technology teams to evaluate and apply emerging capabilities that enhance analytics effectiveness. Maintain awareness of industry trends and identify opportunities to create value through innovation.