Explore

Untitled page

Introduction

Welcome to Synapse Labs' Internship Program! This document provides all the information you need to make the most of the first phase of our internship experience. Our program is designed to give you hands-on experience and help you develop critical skills in computer science and technology.

Company Background

Synapse Labs refers to the brain-inspired concept of a synapse, which is a junction between two nerve cells. It represents the idea of a forward-thinking company dedicated to bridging the gap between human potential and the limitless possibilities of AI technology.

We are professionals dedicated to helping our customers drive growth, deliver exceptional customer & employee experiences, optimize costs, and manage risk through Big Data, Advanced AI, and Machine Learning.

Program Overview

This exclusive internship program provides students the opportunity to solve exciting real-world AI and machine learning challenges. Through the course of the internship, you will have the opportunity to solve real-world problems, test revolutionary technology, and present to industry leaders.

This will be a 10-week, 4 phase internship program.

The four phases include:

Initial Challenge: Candidates will have one week to solve a simpler machine learning problem

Interview: Qualified candidates progress to the interview stage, where we assess their problem-solving and technical skills.

Internship: Interns tackle increasingly complex business problems over a four-week period with support from mentors.

Employment: Successful candidates solve increasingly complex problems for an additional four weeks, receiving monetary compensation.

This document deals specifically with the first phase of the program (Initial Challenge)

Program Structure

Duration: Start and end dates of the internship.

Working Hours: 10-15 hours to finish the first stage

Location: Full virtual and open to anyone

Technical Requirements

Equipment: Computer

Software and Tools: Python, Data Analytics, Machine Learning, Statistics

Onboarding Process

Mentorship and Support

We expect all members to complete the initial design challenge independently, with all necessary information provided in this document. If you need any additional assistance or clarification, please contact us at this email.

recruitment@synapselabs.ai

Evaluation and Feedback

At the conclusion of the one-week period, we will evaluate your solutions based on the provided assessment criteria. Afterward, we will share feedback and inform you of the next steps.

Next Steps?

Based on our assessment criteria, we will notify you if you have progressed to the next stage of the internship program. If your performance meets our standards, you will be invited to an interview with our business experts.

Successful performance in this interview will allow you to advance to the next stage of the program.

There are no rows in this table

⁠

Challenge Description

Objective

Our company is committed to fortifying its risk assessment capabilities to effectively identify and mitigate payment delinquencies by customers. The objective is to enhance our existing analytical frameworks by developing sophisticated models that can discern complex patterns indicative of credit risk. This initiative will integrate diverse data sources, including real-time transactional data and historical customer activity records, to construct a holistic view that significantly improves the precision of our risk evaluations.

Why it matters

Our focus is on deploying these advanced analytics in real-time monitoring systems to ensure stringent regulatory compliance and to allocate our resources more efficiently towards the surveillance of high-risk customers. This strategic enhancement is not only crucial for safeguarding our financial assets but also pivotal in maintaining our market reputation and trustworthiness. By addressing this challenge, we aim to achieve a substantial reduction in financial losses due to delinquencies and enhance operational efficiency, thereby aligning with our strategic goals of industry leadership and customer trust enhancement.

Steps

Step 1: Data Understanding and Preparation

Familiarize with Data: Review the provided data and metadata to understand the structure and contents.

⁠

Datset

⁠

Metadata

⁠

Data Cleaning: Perform basic data cleaning to remove any inconsistencies or missing values.

Step 2: Feature Engineering

Identify Key Features: Determine which features (e.g., transaction frequency, average transaction value) are most relevant for predicting credit risk.

Create New Features: Generate new features that may help improve model performance.

Step 3: Model Development

Make a copy of this notebook for all coding purposes:

Colab Notebook

⁠

Select a Model: Choose a machine learning algorithm (e.g., logistic regression, decision trees) to develop your risk assessment model.

Train the Model: Use the provided data to train your model, ensuring to split the data into training and testing sets.

Step 4: Model Evaluation

Evaluate Performance: Test the model using the testing set and evaluate its performance using metrics like accuracy, precision, recall, and F1-score.

Fine-tune Parameters: Adjust model parameters to improve performance if necessary.

Assessment Criteria

Overfitting/Underfitting Assessment

Compare performance metrics (e.g., accuracy, precision, recall) between training and testing datasets. A large disparity suggests overfitting, while consistently low performance indicates underfitting.

Model Assessment Metric (value)

Measure the overall performance of the model using a specific metric that quantifies how well the model makes predictions. Common metrics include accuracy, precision, recall, F1-score, and AUC-ROC.

Model Explanation Metric

Use techniques such as feature importance scores in tree-based models or coefficients in linear models to quantify the importance of each feature. Visualize these importances to provide a clear explanation.

There are no rows in this table

⁠

Required Information

Datasets

⁠

Datset

⁠

Metadata

⁠

Metadata

⁠

Colab Template

⁠

Colab Notebook

⁠

Key Contacts

⁠

recruitment.synapselabs.ai

⁠

sandeep.bose@synapselabs.ai

There are no rows in this table

⁠

FAQ

Q: How will I know if I have progressed to the next stage of the internship?

A: Based on our assessment criteria, we will notify you if you have moved on to the next stage. If selected, you will be invited to an interview with our business experts.

Q: Who do I contact for technical issues? A: For technical issues, please reach out to recruitment@synapselabs.ai

Want to print your doc?
This is not the way.

Try clicking the ⋯ next to your doc name or using a keyboard shortcut (

CtrlP

) instead.