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CoderPad October Releases

Discover how to run back-end interviews in our new multi-file environment, use code playback for text assessment questions, and test AI-generated follow-up questions to better identify cheating. CoderPad Interview Updates Multi-File Capabilities for Java, Python, Typescript, Ruby and Go. CoderPad multi-file projects offer a highly realistic and flexible environment, where you can add more files, write and run unit tests, install packages from the shell , and more. This is available for Java, Python, Typescript/Javascript, Ruby and Go. Get started by selecting a multi-file project from the languages menu in a pad or sandbox, or create a new question from the dashboard.

Interviewing

3 Spectacular Interview Questions for Junior Backend Developers

Too often, technical interview sessions become mundane rituals rather than insightful evaluations. While this can take its toll on the morale of developers of all skill levels, it’s particularly damaging to junior developers who may become permanently cynical when it comes to following through with future interview processes. For junior roles, it’s vital to strike the right balance between interview questions that challenging enough to keep them interested with making sure the right skills are being assessed. Overly complex problems may not be the best litmus test. Instead, gauging their understanding of basic backend principles and assessing their problem-solving skills can offer more insightful results.

Hiring Developers

How Can You Do Skill-Based Hiring Right—And Wrong?

Traditional hiring methods have their limitations, and can lead to mismatched skill sets and poor job fits. Skill-based hiring, on the other hand, has gained popularity as a way to objectively identify and evaluate candidates’ practical abilities. The thing is, there are right ways and wrong ways to implement skill-based hiring. In this article, we’ll explore the dos and don’ts. The dos of skill-based hiring “Do” build relevant technical assessments

Data Science

8 Red Flags To Watch Out For When Hiring Data Scientists

A CV filled with impressive credentials can capture attention, but it’s the subtleties during an interview that reveal the most about a candidate. Unlike many other fields, data science requires a unique blend of technical expertise, business acumen, and interpersonal skills. Every hiring manager knows the gravity of a wrong hire, especially in a domain as critical as data science. A misfit can not only hinder project progress but can also disrupt team dynamics, making the interview process all the more crucial.

Product Updates

CoderPad September Releases

Discover how you can run your data science interviews with Jupyter Notebook, conduct more realistic interviews with multi-file projects, and benefit from AI-generated follow-up questions. CoderPad Interview Updates Jupyter Notebooks for data science interviews Jupyter Notebooks (Minimal, R, TensorFlow, SciPy) are now available in the pad environment! Run awesome, on-the-job data science interviews using: Multi-file projects for Java, Python 3, and Typescript/Javascript – Open Beta

Data Science

The Differences Between Data Science And Data Engineering Job Roles

Whether charting a career, hiring the right talent, or staying updated with industry shifts, it is important to understand the similarities and differences between data science and data engineering . The goal here is simple: to help you understand what data scientists and data engineers do, especially when scrolling through job listings and the terminology starts to blur together. Even though these roles have “data” in the name, they’re not the same animal. And to make things even more interesting, the lines between them are getting fuzzier, thanks to all sorts of tech advancements.

Coding Challenges

Code Challenge: Hotel Booking Demand

Python is a popular language used by data scientists to carry out data science tasks such as the analysis and reporting of data including data visualizations. At CoderPad, we are excited to announce the integration of Jupyter notebooks to elevate data science interviews that replicate everyday work. For this code challenge, you have been provided with the Hotel Booking Demand dataset which provides valuable insights into customer booking behavior, preferences and trends in the hospitality industry. This two part challenge aims to test your understanding of how you can interact with data and explore it to unveil important hidden information as well as craft visualizations that spotlight various aspects of the dataset.

Hiring Developers

What Developers Love and Hate About Your Hiring Process

The technical hiring process can be a source of frustration and disappointment for both candidates and recruiters. We recently hosted a tech recruitment roundtable , where experienced software engineers and recruitment professionals shed light on what developers dislike about the hiring process vs. what they value and expect. Here are some of the key takeaways: What developers dislike about the hiring process 💔 1. Overemphasis on automation

Data Science

From Data Chaos to Actionable Insights: My Team’s Quest to Hire our First Data Scientist

Editor’s Note: This article was guest-authored by CoderPad’s Senior Principal Engineer Jonathan Geggatt about his data science hiring experience with his previous company, HotelTonight. In the fast-paced business landscape, the ability to harness data effectively can be a game-changer. This became quickly apparent to me during my time as the Director of Data Science and Engineering at HotelTonight, when my team and I identified specific challenges that needed the expertise of a data scientist. Our goal was simple: to transition from a reactionary data team to one that could proactively generate actionable insights and keep a finger on the business’s pulse.

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