Quantum Data Scientist Intern

IBM

Confirmed live today Low trust

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Work type: On-site
Location: —
Employment: Intern
Posted: 1 day ago
Freshness: Confirmed live today

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How this pay compares to similar roles

Similar $167k

$96k most similar roles pay here $235k

This listing doesn't post a salary. Most similar roles pay $112,712–$221,500.

Based on 240 similar postings.

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About IBM

IBM is a US-based global technology company providing hybrid cloud, AI, consulting, enterprise software, and IT infrastructure products and services.

IBM currently has 367 open roles on FindRole.

Listed pay typically runs $181,800–$244,900 across 5 roles with salary data.

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At a glance

TL;DR · Quantum Data Scientist Intern

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As a Quantum Data Scientist Intern on the Data Analytics & Engineering Team, you will work at the intersection of data science, analytics engineering, and platform development. You will support the design and enhancement of scalable data platforms while developing models, dashboards, and self-service solutions to drive strategic decisions. Your daily work involves performing advanced analyses to identify trends, building data pipelines, and collaborating with cross-functional teams to translate business requirements into technical solutions. The role requires proficiency in Python, including libraries like Pandas, NumPy, and scikit-learn, along with SQL for querying large datasets. You will also utilize analytics engineering practices and tools such as dbt, Airflow, Spark, and Presto/Trino. This position focuses on solving complex data reliability and governance challenges within the specialized domain of quantum computing systems and infrastructure.

Skills

Python SQL Pandas NumPy scikit-learn dbt Airflow Apache Spark Presto Trino Git CI/CD Machine Learning Statistical Modeling Data Engineering Analytics Engineering

What you'll do

Support the design, development, and enhancement of scalable data and analytics platforms.
Develop data models, dashboards, and reports to enable informed business decision-making.
Apply analytics engineering best practices to improve data reliability, consistency, and governance.
Perform advanced data analyses to identify trends and actionable business insights.
Design and implement data pipelines and transformations for reporting and analytical use cases.
Communicate findings through visualizations and written reports tailored to technical and non-technical audiences.
Contribute to documentation and the continuous improvement of analytics and data engineering practices.

What we're looking for

Master's Degree (preferred).
Experience with Python, SQL, and data science libraries like Pandas, NumPy, and scikit-learn.
Experience with statistical modeling, forecasting, experimentation, or machine learning techniques.
Strong analytical and problem-solving skills to address ambiguous business questions.
Excellent written and verbal communication skills for technical and non-technical audiences.
Experience with analytics engineering tools like dbt, Airflow, Spark, or Presto/Trino (preferred).
Experience with Git, CI/CD workflows, and large-scale datasets (preferred).

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