Senior Data Scientist – Edge AI Platforms & Systems Analytics

About HP At HP, you’ll have a chance to create tools, technology, and solutions that reshape the way the world works in the future. If you’re looking to join a company that allows you to connect with a network of professionals eager to support you in doing your best work, we want to talk to you. Our legendary culture guides every employee toward success—fostering collaboration and driving innovation. Operating in over 170 countries, HP is always creating new services, products, and capabilities giving you more opportunities to advance your career. Here, innovation is the key to professional development and career mobility. When you join HP, you’re joining a company that believes every voice matters and that we all deserve a seat at the table. From the boardroom to factory floor, we create a culture where everyone is respected and where people can be themselves. You will be part of a global laboratory where different perspectives and experiences will help you solve problems in new ways. This is where you can build a long and wide-ranging career. The Commercial Systems Software Engineering organization is developing next-generation AI software platforms that enable intelligent, secure, and scalable experiences across client and edge computing environments. We are seeking an experienced Senior Data Scientist to lead the development of the data and analytics foundation supporting strategic AI software initiatives. This role will be responsible for integrating data from a wide range of software, hardware, AI, telemetry, performance, and business systems, enabling data-driven decision making across engineering, product, and program management organizations. The successful candidate will combine expertise in data engineering, machine learning, analytics, experimentation, and statistical modeling to deliver insights that improve platform performance, software quality, customer experiences, and business outcomes. This role will work closely with software architects, engineering teams, technical program managers, and leadership teams to establish scalable data pipelines, develop predictive models, and create actionable insights that guide technology and investment decisions. Responsibilities • Design and implement scalable data architectures that support multiple software platform initiatives. • Integrate data across software telemetry, hardware platforms, AI workloads, validation systems, engineering tools, benchmarking environments, and business systems. • Develop and maintain reliable data pipelines, data models, transformation processes, and reporting frameworks. • Define data governance, quality, lineage, security, and lifecycle management standards. • Build machine learning models that identify performance trends, anomalies, optimization opportunities, and operational risks. • Apply advanced statistical methods to understand workload behavior, system utilization, software effectiveness, and customer usage patterns. • Develop workload characterization models and comparative analyses across different platform configurations and operating conditions. • Build predictive models for performance, reliability, capacity planning, and system behavior. • Design and execute experiments that validate hypotheses and measure the effect of software or platform changes. • Develop executive dashboards, scorecards, and visualizations that provide meaningful visibility into platform health and key business metrics. • Partner with engineering teams to translate data-driven insights into product and platform improvements. • Present analytical findings and recommendations to technical teams, business leaders, and executive stakeholders. • Drive data literacy and analytics best practices across the organization. • Mentor engineers and analysts on machine learning, experimentation methodologies, and data science practices. Education & Experience Recommended • Four-year or Graduate Degree in Data Science, Computer Science, Statistics, Applied Mathematics, Computer Engineering, Electrical Engineering, or a related discipline, or equivalent experience. • 8+ years of experience in data science, machine learning, advanced analytics, data engineering, or related technical disciplines. • Proven experience building production-scale analytics platforms, data pipelines, and machine learning solutions. • Experience working with large and complex datasets generated from software systems, AI platforms, cloud environments, or connected devices. • Demonstrated ability to lead analytics initiatives across multiple engineering and business organizations. • Experience supporting data-driven decision making at both technical and leadership levels. Core Data Science & Analytics Expertise • Strong foundation in machine learning, predictive analytics, statistical modeling, forecasting, optimization, clustering, classification, regression, and anomaly detection. • Experience applying supervised and unsupervised machine learning techniques to solve complex business and engineering problems. • Deep understanding of statistical analysis, experimental design, hypothesis testing, model evaluation, and validation methodologies. • Ability to build analytical frameworks that support decision making under uncertainty. • Experience measuring model performance, monitoring drift, and ensuring analytical rigor and reproducibility. • Familiarity with Generative AI, large language models, AI inference workflows, and AI workload characterization. Data Engineering & Platform Skills • Advanced proficiency in Python, SQL, and modern data science libraries and frameworks. • Experience designing and implementing large-scale data pipelines and automated data-processing systems. • Strong knowledge of data architecture, data modeling, ETL/ELT processes, and data integration techniques. • Experience with technologies such as Spark, Databricks, Kafka, Airflow, Snowflake, or equivalent platforms.