As a Quantum Data Analyst Intern 2027, you will join the Adoption Team within IBM Quantum to support the data and analytics mission. You will perform advanced predictive data analysis and build new analytics resources to provide insights into the quantum community and partner engagement with hardware, software, and learning content. This role involves collaborating with internal technical teams and external partners to drive data-driven decisions regarding quantum computing adoption. To succeed, you must be proficient in SQL, relational databases, and Python using libraries like NumPy, Pandas, SciPy, scikit-learn, matplotlib, and Seaborn. You should also possess knowledge of machine learning algorithms, including neural networks and deep learning frameworks like TensorFlow and PyTorch. Additionally, the role requires experience building dashboards with tools such as Metabase or Amplitude to communicate findings clearly.
Perform advanced predictive data analysis to drive insights into IBM Quantum partners and community members.
Build new analytics resources to support reporting on hardware, software, learning content, and events.
Develop dashboards using tools like Metabase or Amplitude to visualize key performance metrics.
Analyze adoption and impact metrics for IBM Quantum products and services.
Translate complex data findings into actionable insights for internal stakeholders and partner institutions.
Manage end-to-end workstreams involving technical, marketing, and business development teams.
Utilize Python libraries and SQL to manage and analyze large datasets from relational databases.
What we're looking for
Master's Degree (preferred).
Pursuing a master's or PhD in data science or a quantitative field like mathematics, computer science, statistics, or physics.
Proficiency with SQL and experience working with relational databases.
Proficiency with Python and libraries such as NumPy, Pandas, SciPy, scikit-learn, matplotlib, and Seaborn.
Knowledge of machine learning algorithms and frameworks including linear regression, decision trees, random forests, gradient boosting, and deep learning frameworks like TensorFlow or PyTorch.
Experience building dashboards with data visualization tools (experience with Metabase or Amplitude is a plus).
Experience with GitHub or other version control platforms (preferred); ability to communicate project insights and propose actionable next steps (preferred).