Manager - Data Science

Expand Energy•Oklahoma City, OK

14d

About The Position

Our core values — Stewardship, Character, Collaborate, Learn, Disrupt — are the lens through which we evaluate every business decision. As a dynamic, growing company that offers extremely competitive compensation and benefits, our employees are our most valued assets and the foundation of Expand's performance among our E&P competitors. We seek applicants from all backgrounds to ensure we get the best, most creative talent on our team. We realize that, historically, underrepresented groups feel the need to be 100% qualified in order to apply. If you meet any combination of our requirements, we encourage you to apply. We strive to hire people from a wide variety of backgrounds, not just because it’s the right thing to do, but because it makes our company stronger. Job Summary Expand Energy is seeking a Data Science Manager to lead the Data Science team and serve as the primary delivery leader within the Fusion Team operating model. This role converts business opportunities into production-grade AI, machine learning, and advanced analytics solutions that deliver measurable enterprise value. The manager leads a multidisciplinary team while partnering closely with business stakeholders, Digital Advancement, IT, and the AI Platform organization.

Requirements

Experience delivering production AI/ML solutions and data products in a business environment, required
Strong understanding of data engineering, machine learning lifecycle management, and software development practices, required
Experience leading cross-functional projects involving business stakeholders, technology teams, and external partners, required
Strong communication, stakeholder management, and organizational leadership skills, required
Demonstrated ability to balance strategic priorities with day-to-day execution, required
Bachelor’s degree - from accredited university - Data Science, Computer Science, Engineering, Statistics, Mathematics, or related field
8 years related work experience in data science, machine learning, analytics, or related disciplines
3+ years of experience leading technical teams and managing direct reports

Nice To Haves

Advanced degree in Data Science, Computer Science, Statistics, Engineering, or related discipline, preferred
Experience with Azure, Snowflake, Git-based development workflows, DevOps pipelines, and modern MLOps practices, preferred
Experience operating in an Agile delivery environment, preferred
Oil and gas industry experience or experience supporting complex industrial operations, preferred
Experience building and scaling AI products from pilot through enterprise deployment, preferred

Responsibilities

Lead the delivery of AI, machine learning, and advanced analytics initiatives from concept through production deployment
Own and maintain a prioritized backlog aligned to business objectives and enterprise AI priorities
Facilitate planning, work prioritization, sprint execution, reviews, and retrospectives
Manage resource allocation and delivery capacity across multiple business domains and competing priorities
Maintain visibility into project status, milestones, dependencies, risks, and issues
Proactively remove obstacles and coordinate cross-functional teams to ensure predictable delivery
Ensure AI, machine learning, and analytics solutions adhere to established development, testing, deployment, and governance standards
Promote disciplined software engineering and MLOps practices, including source control, peer review, testing, and deployment controls
Champion reproducibility, model quality, and operational excellence across delivered solutions
Partner with technical leads and architects on solution design while avoiding becoming a bottleneck for technical decisions
Oversee model performance after deployment and drive remediation for drift, degradation, or technical debt
Partner with business leaders to translate strategic opportunities into practical AI and analytics solutions
Ensure initiatives are aligned to measurable business outcomes and expected value realization
Manage stakeholder expectations, scope, priorities, and delivery commitments
Ensure technical solutions are documented, maintainable, and positioned for sustainable business adoption
Contribute delivery metrics and performance insights that support leadership reporting and value measurement
Ensure data usage and model development comply with company policies and governance requirements
Embed responsible AI practices, model transparency, and documentation standards into day-to-day delivery
Identify and escalate risks related to model performance, data quality, security, compliance, and ethical AI considerations
Promote governance as an integrated part of delivery rather than a downstream approval activity
Lead, coach, and develop a team of data scientists and data engineers
Create opportunities for mentorship, technical growth, and cross-functional learning
Foster an environment of collaboration, innovation, ownership, and continuous learning
Lead Fusion Teams consisting of business stakeholders, Digital Advancement resources, and IT partners
Drive alignment across technical and business teams through shared goals, priorities, and accountability
Collaborate with enterprise platform teams on infrastructure, data architecture, and reusable capabilities
Promote knowledge sharing, best practices, and enterprise-wide adoption of proven AI and analytics patterns
Coordinate effectively with external vendors, implementation partners, and consulting resources when needed