Data Analytics & Data Science Intern — Spring & Summer 2027

Work with real production data. Analyze data generated across Simbe’s robot fleet, retail environments, and operational systems.

Learn modern analytics engineering. Gain experience with SQL, Python, BigQuery, dbt, dimensional modeling, data quality, and BI workflows.

Contribute to meaningful work. Your project will align with an active team initiative rather than being isolated “intern work.”

Own a defined project. You’ll have clear goals and an appropriately scoped problem you can meaningfully contribute to during the internship.

Connect data to real-world outcomes. Help translate robot, store, inventory, service, and operational signals into useful insights and tools.

Learn from experienced data practitioners. You’ll work directly with Simbe’s Data Science & Analytics team and have a dedicated mentor separate from your hiring manager.

Own and contribute to a scoped project aligned with the Data Science & Analytics team’s priorities during your internship.

Write and maintain SQL and Python used for data analysis, transformation, validation, or automation.

Work with data in BigQuery and help transform raw information into trustworthy analytical datasets.

Contribute to dbt models, tests, documentation, and other analytics engineering workflows.

Support dimensional modeling and development of organized data marts for analytical and reporting use cases.

Build or improve dashboards and reporting in tools such as Looker.

Investigate data-quality issues, validate results, and help ensure metrics are accurate and reliable.

Analyze fleet, service, store, or retail data and communicate useful findings to technical and non-technical stakeholders.

Depending on team priorities, contribute to AI-assisted analytics prototypes or tools that make data easier to explore and understand.

Collaborate with Data Science, Engineering, Product, Operations, and other teams to understand data requirements and business context.

Communicate progress, technical decisions, challenges, and results throughout the internship.

Present your project, approach, results, and key learnings at the end of the program.

Currently pursuing a BS, MS, or PhD in Computer Science, Data Science, Statistics, Analytics, Applied Mathematics, Engineering, or a related technical field.

We primarily target students in their final year of study and will also consider strong juniors.

Strong SQL skills and comfort working with structured datasets.

Experience using Python for data analysis, scripting, or automation.

Coursework or hands-on experience in data engineering, analytics, data science, databases, or a related area.

Familiarity with Git or another source-control system.

Ability to investigate data carefully, recognize when results do not make sense, and validate conclusions.

Strong technical problem-solving skills and willingness to work through ambiguous or unfamiliar problems.

Strong written and verbal communication skills.

Experience with BigQuery, Google Cloud Platform, or another cloud data warehouse/platform.

Familiarity with dbt or another SQL-based data transformation framework.

Exposure to dimensional modeling concepts, including star schemas or Kimball-style modeling.

Experience building dashboards or reports in Looker, Tableau, Power BI, or a similar BI tool.

Familiarity with data pipelines, ETL/ELT, workflow orchestration, or tools such as Airflow.

Experience with pandas, Jupyter notebooks, or other Python data tooling.

Experience writing automated data tests, monitoring data quality, or documenting analytical models and metrics.

Personal data, software, analytics, or AI projects outside of coursework.

Previous internship, research, or project experience involving data engineering, analytics engineering, BI, or data science.

Experience using AI-assisted coding, LLMs, or automation tools to accelerate analysis or software development, with good judgment around validating generated output.

Interest in building conversational or natural-language interfaces over structured data.

A defined technical project aligned with current team priorities

A hiring manager responsible for project scope, performance, and feedback

A dedicated mentor separate from the hiring manager

Structured midpoint feedback

Regular opportunities to collaborate with and learn from Simbe engineers and data practitioners

A final project presentation at the end of the internship