A technology company is looking for a Data Scientist who will apply statistical modeling and machine learning to solve complex problems such as predicting job match quality and detecting ghost jobs. This remote-first role is preferred in New York City but flexible in multiple major U.S. cities. The ideal candidate has over 5 years of experience in data science, is proficient with Python and scikit-learn, and possesses strong communication skills. An opportunity to drive impactful decisions within a dynamic team.
Qualifications
5+ years of data science experience with a strong statistics foundation.
Proficiency with Python and statistical modeling.
Experience with NLP and text analysis techniques.
Responsibilities
Develop statistical models for job matching, scoring, and prediction.
Design and analyze A/B tests and experiments.
Build and validate ML models for ghost job detection.
Skills
Data science experience
Statistical modeling
Proficiency with Python
Experience with NLP
Strong communication skills
Tools
scikit-learn
Job description
# Data ScientistRemote-first · NYC preferred Full-time $150k – $200k / year SeniorDataRemote### Apply for this role## About the RoleApply statistical modeling and machine learning to solve RoleStar's hardest problems — predicting job match quality, detecting ghost jobs, and optimizing resume scoring algorithms. This is a remote-first role based in New York City, with the flexibility to work from San Francisco, Austin, Chicago, Seattle, Los Angeles, Boston, or Denver.## Responsibilities* •Develop statistical models for job matching, scoring, and prediction* •Design and analyze A/B tests and experiments* •Build and validate ML models for ghost job detection and ATS analysis* •Communicate findings and recommendations to product and leadership## Requirements* •5+ years of data science experience with a strong statistics foundation* •Proficiency with Python, scikit-learn, and statistical modeling* •Experience with NLP and text analysis techniques* •Strong communication skills and ability to present to non-technical audiencesfor details.