Quantum Data Analyst Intern 2027

About the role

IBM Research is the innovation engine of IBM, exploring what’s next in computing and shaping technologies the world will rely on tomorrow. From advancing AI and hybrid cloud to pioneering practical quantum computing, IBM Research anticipates challenges and unlocks new opportunities for clients, partners, and society. Working in Research means joining a team that accelerates discovery at the intersection of high‑performance computing, AI, quantum, and cloud. You’ll collaborate with leading scientists, engineers, and visionaries to push boundaries and turn ideas into reality. With a culture built on curiosity, creativity, and collaboration, IBM Research offers the opportunity to grow your career while contributing to breakthroughs that transform industries and change the world.

Responsibilities

As a data scientist intern on the IBM Quantum Adoption Team, you will support the data and analytics mission to drive reporting and insights into IBM Quantum partners and community members, enabling data‑driven decisions. Focus areas include adoption and impact of IBM Quantum hardware, software, learning and outreach content, and events. You will interface with client/community‑facing teams at IBM Quantum, technical teams within IBM Quantum, product, marketing, and business development groups, as well as partner institutions. Your work will involve end‑to‑end workstreams such as building new analytics resources and performing advanced (predictive) data analysis. Successful attributes for this role are: creative problem‑framing and solving, self‑starter mindset, agility in navigating a complex organization and stakeholder management, strong organizational and project‑management skills, quick learning with an independent growth mindset, ability to absorb new technical concepts quickly and thoroughly, excellent communication skills to explain technical concepts clearly, and enthusiasm for quantum computing and data science.

Requirements

• Pursuing a master’s or PhD degree in data science or a quantitative field such as mathematics, computer science, statistics, or physics.
• Proficiency with SQL and experience working with relational databases.
• Proficiency with Python and libraries commonly used for data analysis (e.g., NumPy, Pandas, SciPy, scikit‑learn, matplotlib, Seaborn, etc.).
• Knowledge of common machine learning algorithms and frameworks: linear regression, decision trees, random forests, gradient boosting (e.g., XGBoost, LightGBM), neural networks, and deep learning frameworks such as TensorFlow and PyTorch.
• Experience building dashboards with one or more data visualization tools (experience with Metabase and/or Amplitude a plus).

Qualifications

Preferred education: Master’s degree.

Skills

• Experience with GitHub or other version control platform(s).
• Demonstrated ability to communicate project insights and findings and propose actionable next steps.