O'Reilly Auto Parts
Principal Data Scientist - Remote US
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O'Reilly Auto Parts Overview
The Principal Data Scientist advises stakeholders and executives, provides technical leadership across the organization, executes large complex data science projects to solve business problems, and significantly influences the future of data science and AI innovation for the company. Role requires knowledge of tools and methods, including statistics, artificial intelligence, and machine learning, at an advanced level. This position can be worked remotely in the United States. Responsibilities
Define strategy for end-to-end lifecycle of enterprise AI/ML projects, setting architectural standards and deployment methodologies. Drive innovation in designing and implementing advanced, responsible AI validation frameworks with a focus on fairness, transparency, accountability, and explainability, influencing industry best practices. Influence cross-functionally with MLOps, Data Engineering, Application Engineering and Platform teams to establish enterprise-wide standards for scalable, low-latency deployment, robust CI/CD, and lifecycle management across AI initiatives. Define strategy for scalable, sophisticated data science and machine learning solutions, identifying strategic opportunities leveraging GCP, Vertex AI, BigQuery ML, and other cloud platforms. Drive innovation in developing next-generation predictive models and analytical frameworks that provide significant competitive advantage. Define strategy for deep exploratory data analysis (EDA), leveraging cutting-edge tools to uncover transformative patterns, correlations, and anomalies in massive, diverse datasets. Influence cross-functionally to shape enterprise-wide data collection, curation, and governance strategies based on insights from complex data landscapes. Define strategy for aligning all modeling efforts with the enterprise data strategy, long-term business objectives, and emerging technological trends. Drive innovation in architectural design, MLOps pipelines, and data governance, establishing the foundational principles for the organization\'s analytics and modeling capabilities. Influence cross-functionally with executive leadership and key business stakeholders to identify and define high-impact, often ambiguous and unstructured, enterprise-level problems solvable through advanced data science. Define data-driven recommendations that influence business decisions, long-term investments, and ROI; act as a primary thought leader for algorithm-based recommendations across retail operations. Define strategy for continuous learning and development across the data science organization, fostering a culture of intellectual curiosity and innovation. Mentor senior and junior scientists, establish mentorship programs, and contribute to building technical leadership and innovation culture; peer review and publish work in top ML/AI venues where applicable. Define strategy for comprehensive QA and responsible AI testing methodologies, including ethical AI audits, advanced model validation, and proactive drift detection. Drive adoption of advanced tools and automated testing at scale (e.g., Vertex Model Monitoring, CI/CD pipelines). Define strategy for knowledge management, documentation standards, and reproducibility across the data science function, ensuring long-term institutional knowledge and collaboration. Influence cross-functionally to implement tools and processes that facilitate seamless knowledge transfer and version control. Define strategy for communicating complex model outputs into transformative, actionable business insights for stakeholders, directly influencing strategic decision-making. Drive innovation in storytelling with data, leveraging advanced visualization techniques and fostering a data-driven culture across the organization. Define strategy for solving challenging, high-impact business problems using novel algorithms and state-of-the-art AI frameworks (e.g., TensorFlow, PyTorch, HuggingFace). Drive R&D efforts to solve business opportunities across enterprise such as supply chain optimization, personalized consumer experiences, and enhanced operational efficiency. Qualifications
Required Experience in software development and data science, with a focus on AI/ML applications and mathematical modeling. Visionary expertise in advanced data science, machine learning, and AI concepts, pushing the boundaries of current capabilities. Deep knowledge of diverse ML architectures, including cutting-edge research trends. Proven track record of designing and implementing innovative, enterprise-scale data science solutions on cloud platforms like GCP and Azure, leveraging advanced ML capabilities (e.g., Vertex AI operations, distributed training). Exceptional proficiency in Python for highly complex, performant, and scalable AI development. Architectural expertise in cloud-native ML platforms and integration with large-scale data ecosystems. Deep understanding of MLOps, AIOps, and production ML system design at an enterprise level. Ability to innovate and define industry-leading best practices for responsible AI, model governance, and ethical deployment. Expertise in designing complex experimental frameworks for large-scale impact measurement. Exceptional ability to influence executive-level strategy and investment decisions through compelling data-driven insights. Deep understanding of the retail tech industry and its challenges, capable of identifying transformative opportunities for AI. Recognized thought leader in the data science community, internally and externally. Ability to mentor and technically advise Senior and other Principal-level data scientists. Proven experience in building and fostering a culture of innovation and scientific rigor. Preferred Expertise in Python, R, SQL, Scala, and ML/DL libraries such as NumPy, Pandas, scikit-learn, TensorFlow, Keras, and PyTorch. Significant publications or patents in AI/ML or contributions to open-source ML/AI projects. Advanced degrees (Ph.D.) in relevant quantitative fields. Experience in strategic consulting or advisory roles. Past experience solving complex business problems using vision models, NLP, forecasting, and recommender systems. Plans, organizes, prioritizes and oversees activities to efficiently meet objectives. Plans, identifies, monitors, analyzes, and prioritizes risks, creates response plans, and manages risk if it occurs. Compensation and Benefits
Competitive wages and paid time off Stock Purchase Plan and 401k with employer contributions starting day one Medical, dental, and vision insurance with optional Flexible Spending Account (FSA) Wellbeing programs and educational assistance Opportunities for career growth Job Details
Seniority level: Mid-Senior level Employment type: Full-time Job function: Engineering and Information Technology Industries: Retail Equal Employment Opportunity
O’Reilly Auto Parts is an equal opportunity employer. The company does not discriminate on the basis of race, religion, color, national origin or ancestry (including immigration status or citizenship), sex, sexual orientation, gender identity, pregnancy (including childbirth, lactation, and related medical conditions), age (40 and over), veteran status, uniformed service member status, physical or mental disability, genetic information or another protected status as defined by local, state, or federal law, as applicable. Qualified individuals with a disability may be entitled to reasonable accommodation under the Americans with Disabilities Act. If you require a reasonable accommodation during the application or employment process, please contact rar@oreillyauto.com or call 417-862-2674, ext. 68901.
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at
O'Reilly Auto Parts Overview
The Principal Data Scientist advises stakeholders and executives, provides technical leadership across the organization, executes large complex data science projects to solve business problems, and significantly influences the future of data science and AI innovation for the company. Role requires knowledge of tools and methods, including statistics, artificial intelligence, and machine learning, at an advanced level. This position can be worked remotely in the United States. Responsibilities
Define strategy for end-to-end lifecycle of enterprise AI/ML projects, setting architectural standards and deployment methodologies. Drive innovation in designing and implementing advanced, responsible AI validation frameworks with a focus on fairness, transparency, accountability, and explainability, influencing industry best practices. Influence cross-functionally with MLOps, Data Engineering, Application Engineering and Platform teams to establish enterprise-wide standards for scalable, low-latency deployment, robust CI/CD, and lifecycle management across AI initiatives. Define strategy for scalable, sophisticated data science and machine learning solutions, identifying strategic opportunities leveraging GCP, Vertex AI, BigQuery ML, and other cloud platforms. Drive innovation in developing next-generation predictive models and analytical frameworks that provide significant competitive advantage. Define strategy for deep exploratory data analysis (EDA), leveraging cutting-edge tools to uncover transformative patterns, correlations, and anomalies in massive, diverse datasets. Influence cross-functionally to shape enterprise-wide data collection, curation, and governance strategies based on insights from complex data landscapes. Define strategy for aligning all modeling efforts with the enterprise data strategy, long-term business objectives, and emerging technological trends. Drive innovation in architectural design, MLOps pipelines, and data governance, establishing the foundational principles for the organization\'s analytics and modeling capabilities. Influence cross-functionally with executive leadership and key business stakeholders to identify and define high-impact, often ambiguous and unstructured, enterprise-level problems solvable through advanced data science. Define data-driven recommendations that influence business decisions, long-term investments, and ROI; act as a primary thought leader for algorithm-based recommendations across retail operations. Define strategy for continuous learning and development across the data science organization, fostering a culture of intellectual curiosity and innovation. Mentor senior and junior scientists, establish mentorship programs, and contribute to building technical leadership and innovation culture; peer review and publish work in top ML/AI venues where applicable. Define strategy for comprehensive QA and responsible AI testing methodologies, including ethical AI audits, advanced model validation, and proactive drift detection. Drive adoption of advanced tools and automated testing at scale (e.g., Vertex Model Monitoring, CI/CD pipelines). Define strategy for knowledge management, documentation standards, and reproducibility across the data science function, ensuring long-term institutional knowledge and collaboration. Influence cross-functionally to implement tools and processes that facilitate seamless knowledge transfer and version control. Define strategy for communicating complex model outputs into transformative, actionable business insights for stakeholders, directly influencing strategic decision-making. Drive innovation in storytelling with data, leveraging advanced visualization techniques and fostering a data-driven culture across the organization. Define strategy for solving challenging, high-impact business problems using novel algorithms and state-of-the-art AI frameworks (e.g., TensorFlow, PyTorch, HuggingFace). Drive R&D efforts to solve business opportunities across enterprise such as supply chain optimization, personalized consumer experiences, and enhanced operational efficiency. Qualifications
Required Experience in software development and data science, with a focus on AI/ML applications and mathematical modeling. Visionary expertise in advanced data science, machine learning, and AI concepts, pushing the boundaries of current capabilities. Deep knowledge of diverse ML architectures, including cutting-edge research trends. Proven track record of designing and implementing innovative, enterprise-scale data science solutions on cloud platforms like GCP and Azure, leveraging advanced ML capabilities (e.g., Vertex AI operations, distributed training). Exceptional proficiency in Python for highly complex, performant, and scalable AI development. Architectural expertise in cloud-native ML platforms and integration with large-scale data ecosystems. Deep understanding of MLOps, AIOps, and production ML system design at an enterprise level. Ability to innovate and define industry-leading best practices for responsible AI, model governance, and ethical deployment. Expertise in designing complex experimental frameworks for large-scale impact measurement. Exceptional ability to influence executive-level strategy and investment decisions through compelling data-driven insights. Deep understanding of the retail tech industry and its challenges, capable of identifying transformative opportunities for AI. Recognized thought leader in the data science community, internally and externally. Ability to mentor and technically advise Senior and other Principal-level data scientists. Proven experience in building and fostering a culture of innovation and scientific rigor. Preferred Expertise in Python, R, SQL, Scala, and ML/DL libraries such as NumPy, Pandas, scikit-learn, TensorFlow, Keras, and PyTorch. Significant publications or patents in AI/ML or contributions to open-source ML/AI projects. Advanced degrees (Ph.D.) in relevant quantitative fields. Experience in strategic consulting or advisory roles. Past experience solving complex business problems using vision models, NLP, forecasting, and recommender systems. Plans, organizes, prioritizes and oversees activities to efficiently meet objectives. Plans, identifies, monitors, analyzes, and prioritizes risks, creates response plans, and manages risk if it occurs. Compensation and Benefits
Competitive wages and paid time off Stock Purchase Plan and 401k with employer contributions starting day one Medical, dental, and vision insurance with optional Flexible Spending Account (FSA) Wellbeing programs and educational assistance Opportunities for career growth Job Details
Seniority level: Mid-Senior level Employment type: Full-time Job function: Engineering and Information Technology Industries: Retail Equal Employment Opportunity
O’Reilly Auto Parts is an equal opportunity employer. The company does not discriminate on the basis of race, religion, color, national origin or ancestry (including immigration status or citizenship), sex, sexual orientation, gender identity, pregnancy (including childbirth, lactation, and related medical conditions), age (40 and over), veteran status, uniformed service member status, physical or mental disability, genetic information or another protected status as defined by local, state, or federal law, as applicable. Qualified individuals with a disability may be entitled to reasonable accommodation under the Americans with Disabilities Act. If you require a reasonable accommodation during the application or employment process, please contact rar@oreillyauto.com or call 417-862-2674, ext. 68901.
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