Senior Technical Product Owner

Valvoline Global

6d•Remote

About The Position

Valvoline Global Operations, an affiliate of Aramco, is seeking a Senior Technical Product Owner - Data Science & Machine Learning. This role leads data science, machine learning, optimization, forecasting, and advanced analytics products from inception through production and value realization. The position involves managing the DS/ML product roadmap, translating business needs into product outcomes, and balancing various factors like business value, data readiness, technical feasibility, and user adoption. The role requires collaboration with a wide range of stakeholders including business leaders, Data Engineering, Data Science, MLOps, Enterprise Architecture, Security, IT, Finance, and executive stakeholders. The company is moving to a new Global Headquarters in downtown Cincinnati in spring 2027 and this position may be performed remotely with frequent travel to Cincinnati.

Requirements

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.

Nice To Haves

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.

Responsibilities

Own discovery and outcome definition for the DS/ML product portfolio, partnering with business leaders to identify, frame, and prioritize opportunities, and define target users, improvements, 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, using appropriate Agile, hybrid, or program methods.
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, ensuring clarity on business problem, evaluation approach, assumptions, limitations, validation evidence, user acceptance, and production criteria.
Translate technical choices and model performance into business language for 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.
Coordinate communications, training, workflow integration, enhancement, retraining, or retirement when evidence supports a change.
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.