Honor exists to expand the world’s capacity to care.
We’re combining high tech with high-touch to deliver better home care for aging adults, better jobs for Care Professionals, and entirely new offerings to support the aging journey, at scale.
Founded in 2014, and now a Series E funded “Unicorn” valued at over $1B, Honor leads the world’s largest home care network with the most advanced care platform. Our August 2021 acquisition of Home Instead has created a global company that’s revolutionizing how society cares for older adults, their families, and Care Professionals.
The Honor Care Platform combines local care and the most advanced technology to bring the highest quality care to more aging adults.
Grow at Honor as part of a united team where everyone shows up authentically, shares ideas bravely, and solves complex problems!
About the Work:
We’re looking for data scientists to join our team. You'll develop statistical and machine learning models to improve our operations platform and help us provide a better service to our clients. We're looking for generalist data scientists who are excited to jump into new problems and write production-quality code.
- Leverage data to solve meaningful problems with appropriate complexity
- Collaborate with a diverse team across engineering, PM, and care operations to define a strategy and execute against it
- Research operational/logistical problems and proactively identify potential solutions.
- Lead the design, implementation, and evaluation of descriptive and predictive models.
- Integrate machine learning into user-facing applications.
- Mentor and provide technical oversight on teammates' projects throughout the project lifecycle.
- Excellent communication skills with both technical and non-technical peers.
- Excellent mathematical and statistical fundamentals, including a degree in a quantitative field (such as Computer Science, Mathematics, Statistics, Economics, Physics) or equivalent professional experience.
- Wide-ranging professional experience solving complex business problems and shipping Python in a production environment.
- 5+ years of industry experience.
- Expertise with numerical software packages such as NumPy, scikit-learn, or Keras.
- Able to manage product ambiguity, seeking clarity when possible.
- Are accountable end-to-end for your own projects, through planning, deployment, maintenance, and monitoring. You spot and address potential issues early.
Bonus points if you have professional experience with:
- Designing systems to optimize portfolio allocation in a two-sided marketplace (E.g., ideal matches of people needing and providing care, automated financial incentives to staff remaining shifts, etc.)
- Using survival analysis and related methods to evaluate risk of employee churn and to predict future high-performers
- Collaborating with designers to develop effective methods of collecting data in order to quantify highly qualitative attributes, such as personality, taste preferences and perceived quality
- Using NLP methods to build data products from a variety of unstructured data sources, including phone calls and website forms
- Applying spatial statistics to incorporate geographic and regional differences in a variety of problem contexts
Honor is remote friendly! We're hiring across the U.S., with an entirely virtual interview and onboarding process. Most of our positions are remote/work from home and do not require permanent relocation. As conditions allow, we have office space for in-person collaboration in our San Francisco Bay Area, CA and Austin, TX hubs. If you're looking for a great job that offers you the opportunity to work from home, we'd love to talk to you.
Want to know more about why Honor is a great place to work? Check out our perks!
We value people! These four people-centric values guide the ways we work and decisions we make every day.
This role doesn’t sound quite right? Send this application to a friend who may be a fit and check out our other available roles!
Honor is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy), national origin, age, disability, genetic information, political affiliation or belief.