Data Scientist

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Motivo actual: No elegible: remota restringida a USA (exige residir allí). Con pasaporte italiano + settled status (UK) no cumples este requisito sin visado.

Descripción

Who are we and why should you join us? BetterHelp is on a mission to remove the traditional barriers to therapy and make mental health care more accessible to everyone. Founded in 2013, we are now the world’s largest online therapy service, providing affordable and convenient therapy across the globe. Our network of over 30,000 licensed therapists has helped millions of people take ownership of their mental health and change their lives forever. And we’re not stopping there – as the unmet need for mental health services continues to grow, BetterHelp is committed to being part of the solution. As a Data Scientist at BetterHelp, you’ll join a diverse team of licensed clinicians, engineers, product pros, creatives, marketers, and business leaders who share a passion for expanding access to therapy. And as a mental health company, we take employee mental health just as seriously as we do our mission. We deeply invest in our team’s well-being and professional development, because we know that business and individual growth go hand-in-hand. At BetterHelp, you’ll carve your own path, make an immediate impact, and be challenged every day – with a supportive community behind you the whole way. What are we looking for? BetterHelp is looking for a Data Scientist to help us scale and increase our impact. You will use cutting-edge methods to optimize our marketing efforts. The ideal candidate would have experience analyzing large and complex data sets related to web products. You'll be contributing to efforts in marketing inference, including our Marketing Mix Model and Attribution. What will you do? Get insight from an enormous amount of data, and take a proactive role partnering with the marketing team in finding and testing data-driven ideas for improving our efficiency. Contribute to the development, implementation, and maintenance of our marketing models, including a Bayesian Marketing Mix Model and a Multi-Touch Attribution model. Monitor and analyze marketing uplift tests with statistical rigor, present the data and insights to the team, and drive marketing decisions. Look at complex problems and come up with testable models and algorithms that have measurable business impact. Enjoy great teamwork, have lots of fun, and take pride in building a world-class product that makes a difference in people's lives. What will you NOT do? You will NOT worry about "runway", "cash left", or "how much time we have until the next round". We have the startup DNA but we're fully backed and funded, all the way to success. You will NOT be confined to your "job". You will get involved in product, marketing, business strategy, and almost everything we do. You will NOT be bogged down by office politics, ego, or bad attitude. Only positive, pleasure-to-work-with people are allowed here! You will NOT get yourself burned out. We work hard but we believe in maintaining a sustainable work/life balance. Really. Can I work remotely? Yes. We operate on PST and candidates in any time zone are welcome to apply. We ask employees to travel to our San Jose, CA office up to three times per year plus one company-wide offsite to collaborate in person and strengthen working relationships. Travel expenses are covered and reasonable accommodations are made for those under unique circumstances who cannot travel. Requirements BSc/MA in a quantitative discipline such as Statistics, Math, Economics, Computer Science, Operations Research, or Engineering 3+ years of experience in a quantitative data analysis role involving user behavior (e.g. marketing, product, UI/UX) and large data sets Expertise in statistics, especially as it relates to hypothesis testing and experiment design Experience analyzing A/B tests as well as experiments with more advanced statistical methods like difference-in-difference, regression discontinuity, or other methods that deal with network effects or panel data Experience building predictive models and causal inference models Experience analyzing larg

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