Advisory Data Scientist - IBM Quantum

IBM Quantum is building the world’s leading quantum computing systems, software, and cloud services. The Advisory Data Scientist in this role will generate and deliver high-impact insights that inform client-facing teams, guide product strategy, and support executive decision-making across the organization.

You will design and scale analytical models, datasets, and metrics that provide a clear understanding of customer adoption, product usage, and business outcomes. Working closely with client success, product, leadership, and engineering teams, you will translate complex, multi-source data into compelling insights and develop scalable analytics capabilities to enable data-driven decisions within IBM Quantum. Your Role and Responsibilities As an Advisory Data Scientist in IBM Quantum’s Data & Analytics Team, you will design, build, and scale analytical solutions that transform raw data into actionable insights for IBM Quantum.

You will bridge data science, analytics, and data engineering practices to enable high-quality decision-making and democratize access to trusted data.

Your primary responsibilities will include

• Generate Actionable Insights: Analyze complex datasets (e.g. quantum device adoption, platform and product adoption, community adoption) to uncover trends, patterns, and opportunities that inform business decisions.
• Build Analytical Data Models: Design and implement scalable, reusable data models and semantic layers that support self-service analytics, reporting, and advanced analysis. Develop Metrics & KPIs: Define, standardize, and operationalize key metrics across IBM Quantum to ensure consistency and alignment in performance tracking and decision-making.
• Enable Self-Service Analytics: Create curated datasets, dashboards, and tools that empower stakeholders to explore data independently while maintaining governance and quality standards.
• Operationalize Analytics Workflows: Develop and maintain analytical pipelines, ensuring reliability, reproducibility, and scalability of insights.
• Collaborate Cross-Functionally: Partner with product, quantum hardware, software, and client-facing teams to translate business and technical requirements into analytical solutions. Successful attributes to thrive in this role are: Creative in framing and solving complex problems Self-starter Agile in navigating a complex organization and in stakeholder management Organized, with exceptional project management skills Quick learner with an independent growth mindset Able to absorb new technical concepts quickly and thoroughly Good communicator Skilled at fostering teamwork and input from all Able to take ownership of projects and overcome challenges to deliver value Thorough and systematic Enthusiasm about quantum computing and data science Able to ruthlessly prioritize based on business needs/impact 3+ years of experience in data science, analytics, or analytics engineering roles, with a focus on deriving insights from complex datasets. Demonstrated proficiency in SQL (e.g. PostgreSQL, Presto/Trino) for data analysis, transformation, and metric development. Hands-on experience with Python for data analysis and statistical modeling (e.g. pandas, NumPy, SciPy). Proven experience designing and implementing analytical data models (e.g. dimensional models, semantic layers, curated datasets). Experience developing and maintaining production-grade analytical pipelines and workflows (e.g. using Airflow or similar orchestration tools). Strong experience with data visualization and BI tools (e.g. Superset, Cognos Analytics, Tableau), including building dashboards for business and technical stakeholders. Demonstrated ability to define, standardize, and operationalize business and operational metrics across teams. Familiarity with analytics engineering best practices, including data testing, documentation, version control (Git), and modular development. Experience applying statistical methods such as hypothesis testing, trend analysis, and exploratory data analysis (EDA) to inform decision-making. Experience working with heterogeneous datasets. Strong problem-solving and communication skills, with the ability to translate complex data findings into actionable insights for cross-functional stakeholders. Familiarity with Lakehouse architectures and platforms such as IBM watsonx.data. Exposure to machine learning workflows, including feature engineering, model evaluation, and deployment considerations. Understanding of data governance, including metric definitions, lineage, data contracts, and access controls. Experience in cloud or distributed data environments (e.g. hybrid cloud, containerized systems). Familiarity with streaming data concepts and real-time analytics (e.g. Kafka, event-driven architectures). Interest in or exposure to quantum computing, advanced hardware systems, or cutting-edge technology domains. United States Hybrid Professional Multiple Cities