Senior Data Scientist – Edge AI Platforms & Systems Analytics

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.

• Experience working with cloud-based analytics and data platforms.

• Familiarity with MLOps practices including model deployment, monitoring, versioning, automation, and lifecycle management.

• Understanding of telemetry systems, observability frameworks, APIs, and distributed architectures.

AI, Edge Computing & Systems Knowledge

• Understanding of AI and machine learning deployment across client, edge, and cloud environments.

• Familiarity with AI inference workflows and resource utilization across CPU, GPU, NPU, memory, storage, networking, and cloud resources.

• Experience analyzing system telemetry, performance data, workload behavior, and resource utilization patterns.

• Knowledge of software performance measurement, benchmarking methodologies, and workload characterization techniques.

• Understanding of platform software architectures and modern software development practices.

Leadership & Communication Skills

• Strong problem-solving and analytical reasoning skills.

• Ability to transform complex datasets into actionable business and technical recommendations.

• Experience developing executive dashboards, presentations, scorecards, and decision-support materials.

• Proven ability to communicate findings to technical audiences as well as senior leadership.

• Strong written and verbal communication skills with the ability to explain sophisticated analytical concepts clearly.

• Demonstrated ability to influence decisions through data-driven insights.

• Ability to collaborate effectively across engineering, product management, program management, and business teams.

Preferred Qualifications

• Experience supporting AI, edge computing, client platforms, enterprise systems, or software infrastructure programs.

• Experience with telemetry analytics, workload optimization, performance engineering, benchmarking, or predictive maintenance solutions.

• Familiarity with Power BI, Tableau, Grafana, Kibana, or other visualization and reporting platforms.

• Experience analyzing large-scale system, device, or application data.

• Experience working with external technology partners and ecosystem providers.

Impact & Scope

Provides the data, analytics, and machine learning foundation that enables engineering teams and business leaders to make informed decisions across strategic AI software initiatives. Influences platform design, software optimization, investment priorities, product strategy, and executive decision-making through scalable analytics and actionable insights.

Complexity

Works across large-scale datasets spanning software platforms, AI workloads, system telemetry, cloud services, engineering environments, and business systems. Requires balancing data engineering, machine learning, experimentation, analytics, and executive communications while supporting multiple concurrent initiatives and stakeholders.

Salary: $110,300 - $202,450