The focus of the data science team is digitizing the recruiting process - from cutting edge recommendations algorithms, to analytics for performance predictions, to NLP projects for candidate sourcing and profile creation. Vettery data scientists work on decision support, product optimization, and operations scaling projects across these areas. We're growing quickly and this role has the potential for huge impact on our trajectory. You'll be reporting directly to our VP/Head of Data Science and working closely with the product and engineering teams. The ideal candidate is a self-starter, a critical thinker and a good communicator who excels in a fast-paced environment. Join our team and help us build the game-changing data science products that will transform recruiting!
Who you are:
- Ph.D. in Statistics, Operations Research, Mathematics, Computer Science, or other quantitative field.
- 1+ years of industry experience delivering and scaling successful and innovative machine learning products (e.g. recommendation engines, experimentation systems).
- Track record of success working both independently and with key stakeholders to identify and solve data science problems.
- Strong skills in statistical languages (e.g. Python/R) and querying languages (e.g. SQL).
- Experience with NLP a plus! (but not required)
- With the above said, we always encourage people of all backgrounds and experiences to apply. We understand that job listings don't always allow your unique work history to shine so we invite you to show us what you know!
What you'll do:
- Work with a highly motivated, fun and productive team.
- Move quickly and deliver amazing data science products, developing creative solutions to our biggest data science and engineering challenges.
- Build machine learning infrastructure and models (e.g. recommendation engines, statistical models, NLP engines) that drive activity on our platform, scale our business, and enhance the user experience.
- Collaborate with engineering and product leaders, as well as the co-founders, to frame and tackle a problem, both mathematically and within the business context.
- Communicate rationale and findings from analyses to facilitate operational decisions.
- Design and track experiments for data science products as well as analyses throughout the organization.
- In short, own all phases of the data science product lifecycle (exploratory data analysis, model development, model productionizing, rollout, and evaluation)