Principal Data Scientist
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.
About the work:
- 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/maintaining Python in a production environment.
- Using NLP methods to build data products from a variety of unstructured data sources, including phone calls and website forms
- 7+ years of industry experience.
- Expertise with numerical software packages such as NumPy, scikit-learn, or Keras.
- Expertise in managing product ambiguity, seeking clarity when possible
- Foster an environment of end-to-end accountability for team projects, through planning, deployment, maintenance, and monitoring. You level up the team to 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
- Applying spatial statistics to incorporate geographic and regional differences in a variety of problem contexts