Data Scientist
Pearl · Remote · $155k–$175k
The role
Pearl is seeking a Data Scientist to lead advanced research and predictive modeling, focusing on analyzing residential housing and energy performance data. The role involves utilizing causal inference and anomaly detection techniques to improve the accuracy of Pearl's proprietary SCORE models and developing new performance metrics. This position also acts as the primary technical liaison for external research partners and contributes to the dissemination of findings through white papers and publications.
- Pay
- $155k–$175k
- Location
- Remote
- Work mode
- Remote
- Employment
- Contractor
- Level
- Senior
- Experience
- 4+ yrs
- Education
- Masters
What we know that the posting doesn’t say
- Seen todaystill listed on the employer’s careers page
About Pearl
Pearl is a ratings and standards company that rates single-family homes across five key pillars to make home performance visible and valuable in the real estate market.
What you would do
- Manage research with external partners using causal inference methods.
- Analyze residential SCOREs and energy models to detect anomalies.
- Improve predictive accuracy of energy models using utility billing data.
- Identify opportunities to improve SCORE accuracy through feature importance analysis.
- Support development of new performance metrics like Total Cost of Ownership.
- Evaluate integration of climate risk data into the SCORE.
Must have
- Master's degree in Statistics, Economics, Data Science, or related field.
- 4+ years applied experience in statistical analysis and predictive modeling.
- Hands-on experience with causal inference methods.
- Experience with anomaly detection and outlier analysis on large datasets.
- Strong proficiency in Python or R and SQL.
- Experience validating model outputs against ground-truth data.
- Ability to translate statistical findings for non-technical stakeholders.
- Experience working with external consultants or research partners.
Nice to have
- Experience with feature importance analysis and model interpretability.
- Familiarity with housing, real estate, energy, or utility data.
- Experience integrating climate or environmental risk data into models.
- Track record of authoring or co-authoring published research.
- Experience working with large datasets and innovative methodology.
- Comfortable working semi-independently with support.
What you get
- Medical, vision, and dental coverage at no cost for employees and families.
- FSA, HSA, and dependent care accounts.
- Life insurance coverage.
- Employer paid cell phone service.
- 401(k) with employer match up to 4%.
- Stock options.
Experience and education
- 4+ years of relevant experience
- Masters degree or equivalent experience
Key skills
- python
- r
- sql
- causal inference
- regression analysis
- propensity score matching
- instrumental variable methods
- anomaly detection
- outlier analysis
- feature importance analysis
- model interpretability
- predictive modeling
- statistical analysis
- energy modeling
- climate risk data
About the role Pearl is seeking a Data Scientist to lead advanced research and predictive modeling, focusing on analyzing residential housing and energy performance data. The role involves utilizing causal inference and anomaly detection techniques to improve the accuracy of Pearl’s proprietary SCO…
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