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Sr. Data Scientist Roundup: Effects of AI Bias, Impact Hypothesis, and Tips for Hiring Your Data Team
By Emily Wilson • March 26, 2019
When our Sr. Data Scientists aren't teaching the intensive, 12-week bootcamps orcorporate trainingcourses, they're working on a variety of other projects. This monthly blog series tracks and discusses some of their recent activities and accomplishments.
"We have a great opportunity to lay a better foundation for the future of AI if we encourage more women and people of diverse backgrounds to dive into data science and other AI fields," she writes. "And those of us already on the front lines should use this time to think about how we’ll fight against bias in our own domains. The AI revolution is set to change the world; let’s make sure it’s for the better."
Kerstin Frailey, Metis Sr. Data Science - Corporate Training
Too often we assume that good data science translates to effective data science, even though it's untrue, according to Metis Sr. Data Scientist Kerstin Frailey. In her latest blog post, she writes about The Impact Hypothesis, which she considers the "keystone to transformative data science." In essence, the hypothesis explains how to critically scope and communicate how a project will drive impact for your business, ensuring both good and effective data science occurs.
Brendan Herger, Metis Sr. Data Scientist - Corporate Training
As data continues to change the way companies do business, the task of attracting (and retaining) data science talent can be difficult. In his latest blog post, Metis Sr. Data Scientist Brendan Herger draws from years of experience to provide a playbook for how to effectively and efficiently hire data scientists and build out your team.
Let’s face it: today's job market is confusing – perhaps even more so in data science, where job titles are all over the map, needed skill sets aren’t always clear in job descriptions, interviews are famously intense, and so forth. In this post, Metis Seattle Career Advisor Marybeth Redmond explains how to effectively navigate the tricky waters of the job search, particularly in the Seattle area.
Data scientists are often described as hybrids: part statistician, part computer scientist; part analyst, part strategist. But while we focus on the myriad technical skills that a data scientist should possess, we often overlook one of the foundational skills (without which the whole edifice falls apart): communication skills. In this post, read three tips for improving your professional communication skills.
When our Sr. Data Scientists aren't teaching the intensive, 12-week bootcamps or corporate training courses, they're working on a variety of other projects. This monthly blog series tracks some of their recent activities. In this edition, read blogs on the importance of both scoping and designing data science projects, and get an introduction to PyTorch with NLP.