Kensho | <a href=" | Primarily: Boston, MA (Cambridge). Case-by-case: New York (NYC) | ONSITE | FULL TIME
Kensho is applying machine learning and quantitative modeling to timeseries, graph and unstructured data to make computer driven insights faster, more accessible, intuitive and beautiful.
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+ Software Engineers -- Create beautiful web apps, dynamic visualizations, meaningful and non-flaky tests, composible and scalable infrastructure, cutting edge site reliability (SRE), neatly flexible operational frameworks, thoughtful APIs, practical yet robust security, and powerful frameworks for data processing.
+ Machine Learning Engineers and Scientists -- Create advanced machine learning pipelines, NLP systems and new data modeling techniques at scale using python, R or similar.
Our Interview Process:
* We hope you'll share a project, paper or resume with us that highlights where you shine, with a short note so we can appreciate you as a person.  Please say hi at REDACTED_EMAIL or <a href="
* As a small team, we'll reach back out to a few individuals to chat with a team member via phone, video or, if you are local, in person--to show and discuss your work, projects and code
* We may ask you to do a programming or data science challenge (&lt;= 4 hours)
* We'll invite you to our Harvard Sq. headquarters to meet more of the team, where we hope you'll interview us too
* We'll discover we are peanut butter and jelly together, and wish we'd met sooner
* Having made you a non-exploding offer, we think you'll want to sign it
* You'll join us and have a lot of fun, get to play with fascinating data, models and technology alongside delightful, hungry and creative people
* Something about being on a mission to change the world (hey, we're a start up)
Who We Interview:
We scan your resume for at least one outlier experience, be it your undergraduate CS program, PhD program, open source contributions, research, publications, or previous/current employment.
Stack:
Functional javascript (react, canvas), python (numpy, pandas, scikit-learn et. al.)