Lyft's mission is to improve people's lives with the world's best transportation and it'll be a slow slog to get there with dispatchers manually matching riders with drivers. We need automated decision making, and we need to scale it in a way that optimizes both the user experience and the market efficiency. Complementing our Science roles, an engineer with a knack for practical machine learning and an eye for business impact can help independently build and productionize models that power product experiences that make for an enjoyable commute. A year and a half ago when we began scouting for this type of machine learning-savvy engineer --something we now call the machine learning Software Engineer (ML SWE) -- it wasn't something we knew much about. We looked at other companies' equivalent roles but they weren't exactly contextualized to Lyft's business setting. This need motivated an entirely new role that we set up and started hiring for. Most companies are open about the expectations for the role being interviewed for, the interview process, and preparation tips.
You've spent months studying data science, now it's time to find a job in the industry. Fortunately, companies all over the world are looking to hire data scientists -- and fast. According to LinkedIn's 2020 U.S. Emerging Jobs Report, skills related to Machine Learning, Deep Learning, TensorFlow, Python, Natural Language Processing, etc. seen more than 70% annual growth. According to an IBM survey, the openings for data and analytics talent in the US will continue to increase, reaching 133% growth in 2020, and creating more than 700,000 openings. Qualified candidates will have a multitude of vacancies to choose from when ready to seek out a new position in the field.
Interviewing is broken. Karat professionalizes interviewing. Hiring top talent is a critical activity for all companies, yet the way organizations interview candidates is broken. Interviewing is a time consuming process that is rarely data-driven. Here at Karat, we see a massive opportunity to transform the interviewing experience for every candidate and company. Karat is on a mission to assess the world's talent. We are the first dedicated marketplace for technical interviewers. Karat's network of seasoned engineers conduct the first rounds of technical interviews for elite engineering companies. Our robust platform saves teams thousands of valuable hours while allowing them to focus on the top performing candidates. Karat's unique approach recognizes that people are central to the hiring process and that they can be supercharged by leveraging machine learning and our rich database of the world's interviews. We face incredible demand for our service and are delivering significant value to elite engineering companies like Interana, BuildZoom and Minted. We are funded by top VCs including Formation8 and Founder Collective, plus the founders of companies like Glassdoor, Mulesoft, Lookout, OPOWER, MediaLink and CAA. Karat is headquartered in the University District of Seattle, WA. Join our elite community of Expert Interviewers. Karat’s Expert Interviewers are recognized and rewarded for doing a first-class job as top assessors of technical talent. Every interviewer in the network is an accomplished engineer. Our interviewers include development managers from big-tech companies, start-up engineers and freelancers covering the full technology stack. Flexible, high impact work that is compensated at highly competitive rates. As an Expert Interviewer, you will be compensated at highly competitive rates for your interviewing expertise. The time commitment is flexible—many of our interviews happen on nights and weekends. Some experts do 5 interviews/week while others do over 20 interviews/week. You can work from anywhere, anytime. You will sharpen your interviewing skills and transform the interviewing experience for every candidate and company. Who are we looking for? We are looking for experienced software engineers who believe that interviewing is a first-class job. You should possess: Experience as a top performing engineer at a big-tech or start-up. Significant interviewing experience focused on evaluating fundamental computer science skills (i.e. data structures, algorithms etc.), software craftsmanship (i.e. understanding of unit testing, source control, APIs etc.), and/or specific technologies (i.e. iOS, distributed systems etc.). Strong oral and written communication skills. Able to empathize with candidates and provide actionable feedback. An ability to structure your schedule (i.e. you can pick certain blocks of time during the day, evenings, weekends). A genuine desire to continuously improve the Karat service and technical interviewing. Interested? Apply below to learn more and connect with the Karat Team.
All humans have bias, unconscious or otherwise – the risks and effects of which have never been more apparent than in recruitment. As such, it seems reasonable to hope that technology holds the key to achieving fairer outcomes in hiring decisions. For those committed to making the process more inclusive and organisations more diverse, the potential and ever increasing possibilities for technology as part of recruitment can appear limitless. As the use of AI in recruitment continues to hit the headlines, automation in recruitment has become more widespread, transforming who and how companies recruit. Although the appeal of AI is clear (and its use can be transformative in recruitment), it is important to reappraise exactly what technology can change for the better right now. By taking a more realistic look at the technology, we can recalibrate our expectations and understand the role we have to play in driving meaningful change.
According to the LinkedIn 2017 U.S. Emerging Jobs Report, data scientist positions grew 6.5 times in the last five years. So, aspiring candidates with proper training will have multiple vacancies to choose from when seeking out a data science position. All that stands in your way is the interview. Yes, interviews are extremely nerve-wracking, but more so in the case of data scientist positions as only research and preparation will help you ace the big day. But in data science, being an application-based discipline, the expectations vary considerably from one industry to another.