Data Scientist Student 2026 H2 (780582)
Career Opportunities: Data Scientist Student 2026 H2 (780582)
Requisition ID 780582 - Posted - Country/Area (1) - state/province (1)Grow with us
Our Exciting Opportunity We are seeking a technically curious Data Science Intern to join our applied analytics team. You will work with experienced data scientists and engineers to turn data into actionable insights and help deliver reproducible, well-tested analyses. This internship is hands-on: you’ll contribute to model development, experiment design, and end-to-end workflows while learning production-aware practices such as version control, containerization, and basic deployment patterns.
What you will do
- Translate business problems into measurable data questions and define evaluation criteria (metrics, baselines).
- Prepare and engineer features from structured and unstructured sources; write clear, reproducible data pipelines and SQL queries.
- Implement, train, and evaluate models (regression, classification, clustering, simple neural networks) with attention to cross-validation, hyperparameter tuning, and performance monitoring.
- Develop notebooks and modular Python code (with unit tests and documentation) and help package work for reproducibility (Git, virtual environments, Docker basics).
- Produce clear visualizations and model summaries to communicate results and limitations to stakeholders.
- Collaborate in code reviews, experiment tracking, and model validation; learn essentials of MLOps (model versioning, basic deployment concepts).
To be successful in the role you should have
Education & Knowledge:
- Currently enrolled in or recently graduated from Data Science, Computer Science, Statistics, Mathematics, Engineering, or related field.
- Solid grounding in statistics, basic probability, and core machine learning concepts (bias/variance, regularization, evaluation metrics).
Skills & Tools
- Proficient in Python and familiar with common libraries (pandas, numpy). Strong emphasis on writing modular, readable code.
- Experience with Jupyter notebooks and at least one ML library (scikit-learn required; exposure to PyTorch or TensorFlow is a plus).
- Comfortable writing SQL for data extraction and basic familiarity with data ingestion/ETL concepts.
- Basic data visualization skills (matplotlib, seaborn, or equivalent) and ability to interpret model diagnostics.
- Familiarity with Git for version control; interest in containerization (Docker) and cloud concepts (AWS/GCP/Azure) is advantageous.
Preferred:
- Coursework or projects demonstrating model evaluation, cross-validation, hyperparameter search, or simple deep learning.
- Exposure to experiment tracking tools, collaborative development (code reviews), or basic model deployment workflows.
- Strong communication skills and eagerness to adopt software engineering best practices.
What we offer:
- Mentorship from experienced data scientists and engineers.
- Hands-on work on end-to-end projects with real datasets and measurable business impact.
- Opportunity to learn production-aware data science practices and advanced techniques.
- A collaborative environment where technical growth and best practices are encouraged.
Duration: 6 months (flexible)
Join our Team
Email this job to a friend The job has been sent to The job has been sent toGrow with us
Our Exciting Opportunity We are seeking a technically curious Data Science Intern to join our applied analytics team. You will work with experienced data scientists and engineers to turn data into actionable insights and help deliver reproducible, well-tested analyses. This internship is hands-on: you’ll contribute to model development, experiment design, and end-to-end workflows while learning production-aware practices such as version control, containerization, and basic deployment patterns.
What you will do
- Translate business problems into measurable data questions and define evaluation criteria (metrics, baselines).
- Prepare and engineer features from structured and unstructured sources; write clear, reproducible data pipelines and SQL queries.
- Implement, train, and evaluate models (regression, classification, clustering, simple neural networks) with attention to cross-validation, hyperparameter tuning, and performance monitoring.
- Develop notebooks and modular Python code (with unit tests and documentation) and help package work for reproducibility (Git, virtual environments, Docker basics).
- Produce clear visualizations and model summaries to communicate results and limitations to stakeholders.
- Collaborate in code reviews, experiment tracking, and model validation; learn essentials of MLOps (model versioning, basic deployment concepts).
To be successful in the role you should have
Education & Knowledge:
- Currently enrolled in or recently graduated from Data Science, Computer Science, Statistics, Mathematics, Engineering, or related field.
- Solid grounding in statistics, basic probability, and core machine learning concepts (bias/variance, regularization, evaluation metrics).
Skills & Tools
- Proficient in Python and familiar with common libraries (pandas, numpy). Strong emphasis on writing modular, readable code.
- Experience with Jupyter notebooks and at least one ML library (scikit-learn required; exposure to PyTorch or TensorFlow is a plus).
- Comfortable writing SQL for data extraction and basic familiarity with data ingestion/ETL concepts.
- Basic data visualization skills (matplotlib, seaborn, or equivalent) and ability to interpret model diagnostics.
- Familiarity with Git for version control; interest in containerization (Docker) and cloud concepts (AWS/GCP/Azure) is advantageous.
Preferred:
- Coursework or projects demonstrating model evaluation, cross-validation, hyperparameter search, or simple deep learning.
- Exposure to experiment tracking tools, collaborative development (code reviews), or basic model deployment workflows.
- Strong communication skills and eagerness to adopt software engineering best practices.
What we offer:
- Mentorship from experienced data scientists and engineers.
- Hands-on work on end-to-end projects with real datasets and measurable business impact.
- Opportunity to learn production-aware data science practices and advanced techniques.
- A collaborative environment where technical growth and best practices are encouraged.
Duration: 6 months (flexible)