The Challenge

 

We believe in hands-on assessment that demonstrates real problem-solving abilities. Our technical assessment is designed to evaluate your skills in both traditional software development and machine learning, while requiring original thinking that goes beyond what can be accomplished with AI tools alone.

 

Assessment Overview

 

Task: Anomaly Detection System with Explainability

 

Design and implement a real-time anomaly detection system for time-series data that includes:

 

Backend Implementation

​

• Create a streaming data pipeline that processes incoming data

• Implement both traditional statistical and ML-based anomaly detection methods

• Design an API that serves both detection results and explanations

 

Frontend Visualization

​

• Build an interactive dashboard showing real-time data streams

• Implement visualization of anomaly detection results

• Create an explainability interface that helps users understand why specific points were flagged

 

Novel Algorithm Component

​

• Develop a custom scoring mechanism that combines multiple detection methods

• Implement a new approach to reduce false positives while maintaining sensitivity

• Document your reasoning and methodology

 

Key Requirements

 

• Use Python for the backend (FastAPI/Flask) and Reflex.dev or React for the frontend

• Implement at least one custom ML model (not just imported from a library)

• Include tests and documentation

• Provide explanation of your design choices and trade-offs

• Feel free to use any additional libraries or tools you think are appropriate

• We value clean, maintainable code over premature optimization

• Questions about the assessment can be sent to careers@alphanome.ai

 

Evaluation Criteria

 

We will assess your submission based on:

 

Technical Implementation (40%)

• Code quality and organization

• System architecture decisions

• Performance and scalability considerations

 

Algorithm Design (30%)

• Originality of approach

• Effectiveness of the solution

• Quality of documentation and explanations

 

Frontend Implementation (20%)

•  User interface design

•  Visualization effectiveness

•  Interactive features

 

Overall Quality (10%)

•  Code documentation

•  Testing approach

•  Submission presentation

 

Submission Process

 

• Fork our template repository (link provided after application)

• Complete the implementation

• Submit a pull request with your solution

• Include a README.md with:

  • Setup instructions

  • Architecture overview

  • Key decisions and trade-offs

  • Future improvements

 

Ready to take on the challenge? Next Steps

 

• Apply below

• If your profile matches our requirements, we'll send you the detailed assessment instructions and link to the template repository

• Take up to one week to submit your solution

• Our team will review and provide feedback within 5 business days

• Successful candidates will be invited for a technical discussion about their solution