Advisory Data Scientist - IBM Quantum

IBM; New York (NY); On-site; USD 120,000 - 180,000; Full time

Job summary

IBM Quantum is seeking an Advisory Data Scientist in the Data & Analytics Team to design, build, and scale analytical solutions that convert raw data into actionable insights. You will collaborate across product, hardware, software, and client-facing teams to drive data-driven decisions and democratize trusted data.

You will develop metrics, semantic models, and scalable analytics capabilities to quantify customer adoption, product usage, and business outcomes, with an emphasis on reusable data

Qualifications

• 3+ years of data science or analytics engineering experience.
• Proficiency in SQL for data analysis, transformation, and metric development.
• Hands-on Python for data analysis and statistical modeling.
• Experience designing and implementing analytical data models.
• Experience building production-grade data pipelines and workflows.
• Strong BI and data visualization experience for business stakeholders.

Responsibilities

• Design, build, and scale analytical solutions transforming raw data into actionable insights.
• Bridge data science, analytics, and data engineering to enable data-driven decisions.
• Develop scalable data models and semantic layers for self-serve analytics.
• Create curated datasets, dashboards, and tools with governance.
• Operationalize analytics workflows ensuring reliability and reproducibility.
• Collaborate with product, hardware, software, and client-facing teams to translate requirements.

Skills

Data science

SQL

Python

Analytics engineering

Communication

Problem solving

Stakeholder management

Education

Master's Degree

Tools

SQL (PostgreSQL, Presto/Trino)

Python (pandas, NumPy, SciPy)

Airflow

Tableau

Superset

Cognos Analytics

Job description

Introduction

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

Preferred Education

Master's Degree

Required Technical And Professional Expertise

• 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.

Preferred Technical And Professional Experience

• 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.