Data Science & ML coding assessments on CodeSubmit

Hire Data Scientists & Machine Learning Engineers with Confidence

Let your candidates showcase their data chops with full support for Jupyter notebooks, Python, R, PyTorch, TensorFlow, Spark, and Julia.

CodeSubmit - The #1 Take-home Coding Challenge & Live Coding Interview Platform for Data Science & ML
Data Science Developer
ML Developer
Real-World Scenarios to Evaluate Data Science Competence

Refine Your Hiring Process with Data-Driven Assessments

Enhance your recruitment approach by utilizing CodeSubmit's Jupyter Support, enabling candidates to demonstrate their expertise in a practical, real-world context. Assess proficiency in a range of technologies including Python, R, and various machine learning frameworks, ensuring a comprehensive evaluation of skills.

Our platform facilitates an environment that mirrors real-world challenges, allowing you to gauge the practical abilities of candidates effectively and make informed hiring decisions.

Jupyter Minimal: Utilize a JupyterLab environment to enable candidates to work on notebooks collaboratively, ensuring a focus on practical skills.

Jupyter SciPy: Access a JupyterLab setup with SciPy to provide a robust assessment environment, incorporating key Python scientific packages.

Jupyter TensorFlow: Evaluate candidates on machine learning model development within a JupyterLab environment equipped with TensorFlow.

Hugging Face
Downloading Dataset...80%
CodeSubmit - Data Science & ML Coding Assessments
Assess Proficiency in R, PyTorch, and Julia

Advanced Evaluation with Specialized Tools

Jupyter R: Provide an environment where candidates can showcase their R programming skills, complete with essential packages.

Jupyter PyTorch: Offer a platform for candidates to demonstrate their knowledge in deep learning, using a PyTorch-based JupyterLab environment.

Jupyter Julia: Enable candidates to exhibit their technical computing abilities with Julia in a high-performance JupyterLab setting.

"Enhancing our ML hiring process with CodeSubmit led to more insightful assessments and efficient candidate selection."