Which role are you hiring for?
We have a wide range of tests that can help you find the best candidates for your organization. Choose the role that best fits your needs and let us help you find the right talent.
Data Science Fundamentals
Entry-level assessment of basic data analysis concepts, statistics, and Python fundamentals for beginners in data science.
ML Concepts Introduction
Beginner-friendly assessment of fundamental machine learning concepts, algorithms, and basic model evaluation metrics.
Data Visualization Basics
Entry-level assessment of data visualization techniques, chart selection, and basic implementation using popular libraries.
SQL for Data Analysis
Basic SQL assessment for data analysis, covering fundamental queries, joins, and simple aggregations relevant to ML/AI projects.
Practical Machine Learning
Foundation-level test on implementing common machine learning algorithms, feature engineering, and basic model evaluation techniques.
Data Wrangling and Preprocessing
Amateur-level assessment focusing on data cleaning, transformation, feature engineering, and preparation for ML modeling.
Statistical Methods for Data Science
Foundation-level assessment of statistical concepts and methods commonly applied in data science and machine learning projects.
Neural Networks Fundamentals
Amateur-level introduction to neural networks covering architectures, backpropagation, activation functions, and basic implementation.
Deep Learning Applications
Mid-level assessment of deep learning techniques for computer vision, NLP, and time series analysis with practical implementation.
Natural Language Processing Techniques
Intermediate assessment of NLP methods including text preprocessing, embeddings, sequence models, and practical applications.
Computer Vision Fundamentals
Mid-level assessment of computer vision concepts, image processing techniques, and CNN architectures for visual data analysis.
Machine Learning System Design
Intermediate assessment of ML system architecture, pipelines, deployment considerations, and operational best practices.
Time Series Analysis and Forecasting
Mid-level assessment of time series methods, models, and forecasting techniques for temporal data analysis.
Unsupervised Learning Techniques
Intermediate assessment of clustering, dimensionality reduction, anomaly detection, and other unsupervised learning methods.
Feature Engineering and Selection
Mid-level assessment focused on advanced feature creation, transformation, selection techniques, and their impact on model performance.
MLOps Fundamentals
Intermediate assessment of machine learning operations, covering CI/CD for ML, model monitoring, experiment tracking, and deployment.
Advanced Deep Learning Architectures
Professional-level assessment of cutting-edge neural network architectures, implementation strategies, and optimization techniques.
Reinforcement Learning Systems
Professional-level assessment of reinforcement learning algorithms, environments, policy optimization, and practical applications.
Large Language Model Engineering
Professional assessment focusing on working with large language models including prompt engineering, fine-tuning, and deployment strategies.
Advanced Computer Vision Systems
Professional-level assessment of advanced computer vision techniques, object detection architectures, segmentation, and video analysis.
ML System Optimization
Professional assessment focusing on optimizing machine learning systems for performance, scalability, and efficiency in production.
Advanced Natural Language Understanding
Professional-level assessment of cutting-edge NLP techniques including semantic understanding, question answering, and language generation.
Causal Inference and ML
Professional assessment focusing on causal inference methods, counterfactual analysis, and causality-aware machine learning approaches.
Bayesian Machine Learning
Professional-level assessment of Bayesian methods in machine learning, probabilistic programming, and uncertainty quantification.
AI Research Methods
Elite-level assessment of machine learning research methodologies, experimental design, and novel contribution development.
AI System Architecture
Advanced assessment of large-scale AI system design, focusing on integrating multiple AI components into robust end-to-end systems.
Multimodal AI Systems
Elite assessment of multimodal learning architectures, fusion techniques, and systems that combine vision, language, audio, and other modalities.
Generative AI Mastery
Advanced assessment focusing on cutting-edge generative models, diffusion processes, and creative AI applications.