At Valvoline Global Operations, we're proud to be The Original Motor Oil, but we've never rested on being first. Founded in 1866, we introduced the world's first branded motor oil, staking our claim as a pioneer in the automotive and industrial solutions industry. Today, as an affiliate of Aramco, one of the world's largest integrated energy and chemicals companies, we are driven by innovation and committed to creating sustainable solutions for a better future.

The Senior Technical Product Owner – Data Science & Machine Learning leads data science, machine learning, optimization, forecasting, and advanced analytics products from initial business opportunity through production use, ongoing monitoring, and value realization. Working across business stakeholders, Data Engineering, Data Science, MLOps, Enterprise Architecture, Security, and other technology partners, the incumbent manages the DS/ML product roadmap and converts ambiguous business problems into clear product outcomes, requirements, and delivery decisions.

How You Will Make an Impact

• Own discovery and outcome definition for the DS/ML product portfolio. Partner with business leaders to identify, frame, and prioritize opportunities; define target users, the decisions or workflows to improve, measurable business outcomes, adoption expectations, guardrails, and decision rights. Maintain a multi-year vision and prioritized roadmap aligned with enterprise strategy and regional needs.
• Own the product brief, prioritized backlog, requirements, acceptance criteria, milestones, dependencies, risks, and stage-gate decisions for assigned products. Coordinate Data Scientists, Data Engineers, MLOps Engineers, analysts, business subject-matter experts, Architecture, Security, IT, Business Chance Management, and other delivery partners from discovery through validation and controlled release. Use Agile, hybrid, or program methods appropriate to the work.
• Lead data availability and readiness assessments with Data Engineering, data owners, and governance partners, including source coverage, quality, history, access, privacy, refresh frequency, lineage, and suitability for the intended use. Surface feasibility gaps and tradeoffs early and help stakeholders make informed scope, sequencing, investment, and expected-return decisions.
• Partner with Data Scientists and technical leads throughout experimentation and model development. Ensure the business problem, evaluation approach, assumptions, limitations, validation evidence, user acceptance, and production criteria remain clear; translate technical choices and model performance into business language for the appropriate audiences.
• Partner with MLOps and engineering teams to define deployment, model serving, integration, security, support, rollback, retraining, release, and production-readiness requirements. Ensure each production model has clear ownership, documentation, service expectations, escalation paths, monitoring thresholds, and an agreed operating model.
• Track model performance, data quality, drift, reliability, latency, cost, adoption, realized value, and stakeholder feedback after launch, as applicable. Coordinate communications, training, workflow integration, enhancement, retraining, or retirement when evidence supports a change, and prepare stakeholder readouts that connect technical performance to business outcomes.
• Maintain an enterprise view of the DS/ML product portfolio and provide regular executive updates on status, expected value, actual results, risks, dependencies, and decisions needed. Promote consistent product-management practices, responsible ML expectations, transparent decision making, and continuous improvement across the team.

What You'll Need

• Bachelor's degree in Business, Computer Science, Engineering, Data Science, Analytics, Information Systems, or a related field, or an equivalent combination of education and experience.
• Approximately 6-7 years of progressive experience in technical product management, product ownership, or program/project management in data-intensive environments.
• At least 3 years owning data science, machine learning, optimization, forecasting, or advanced analytics products.
• Experience guiding DS/ML initiatives from discovery and data-readiness assessment through development, deployment, monitoring, adoption, and value realization.
• Project and/or program management experience coordinating workstreams, dependencies, stakeholders, and executive communications.
• Working knowledge of the DS/ML lifecycle, including problem framing, data readiness, experimentation, validation, deployment, monitoring, drift, retraining, and retirement.
• Understands data quality, privacy, lineage, security, integration, model evaluation, service expectations, and production operating models well enough to guide decisions without substituting for technical specialists.
• Ability to influence without authority, build trust across technical and business teams, and communicate effectively with executives and practitioners.
• Demonstrate curiosity, sound judgment, inclusive collaboration, and a commitment to feedback and continuous improvement.

What Sets You Apart

• A record of moving Data Science/Machine Learning products beyond experimentation into sustained business use with measurable outcomes.
• Experience operating in a large, matrixed global enterprise while preserving clarity, momentum, and accountability across the product lifecycle.
• Experience working across regions, functions, and distributed technical teams.
• Experience with Databricks and AWS; SAP-based data landscapes are beneficial.

Benefits That Drive Themselves

• Health insurance plans (medical, dental, vision)
• Health Savings Account (with employer-base deposit and match)
• Flexible spending accounts
• Competitive 401(k) with generous employer base deposit and match
• Incentive opportunity
• Life insurance
• Short- and long-term disability insurance
• Paid vacation and holidays
• Employee Assistance Program
• Employee discounts
• PTO Buy/Sell Options
• Tuition reimbursement
• Adoption assistance