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.
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.
Apple Inc. is one of the biggest technology companies in the world that designs, develops, and sells consumer electronics, computer software, and online services. Apple is constantly in need of creative, passionate, and dedicated data scientists that can sit on any number of their teams. From its researched-based artificial intelligence development team at Siri to cloud-base architecture development team at iCloud, Apple has slowly but steadily been building data science teams to handle the avalanche of data accumulated on a daily basis. As with other big tech companies, the role of a data scientist at Apple varies a lot and is dependent on the teams you are assigned to. This means the job will require everything from analytics to machine learning software design to plain engineering.
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.