Arlo
Data Scientist
New York City
Sponsorship not specifiedDetected 90 days ago
PythonAlgorithmsSQLMachine Learningscikit-learnPandasdbtData EngineeringData ScienceSalesPipeline ManagementAuditingCollaborationActuarial ScienceUnderwritingPipeline Integrity
About the role
- Arlo quotes small businesses using AI-powered underwriting that predicts individual risk to determine group rates.
- But accurate pricing requires more than historical data alone - at the time of quoting, additional signals like prior rates, aggregate claims history, and self-reported health information can meaningfully sharpen our view of a group's risk profile.
- Analyze early-period claims data to surface patterns that indicate risk was underestimated at quoting - and quantify the gap.
Responsibilities
- middlemen taking a cut, fraud nobody stops, and billing systems designed to fight over payment instead of deliver care.
- Upstream data partnerships. Work with engineering and data teams to document known data quality issues, prioritize fixes, and maintain a clear picture of what the data can and cannot reliably support at any given point.
- Rate determination. Develop calibration factors that translate soft signals - based on competitive information, econometrics and emerging events - into rate adjustments layered on top of core model output.
- Multi-signal fusion. Design models that ingest heterogeneous inputs and synthesize them into an enriched risk view that reduces reliance on manual review.
- Confidence scoring. Build the logic that decides when a quote can be issued automatically versus routed to a human, minimizing queue volume without increasing adverse selection.
- Threshold and rule design. Collaborate with underwriting to set and validate auto-issuance decision thresholds, and monitor their performance over time.
- Consistency monitoring. Build checks and alerting for data inconsistencies across our ingestion layer: duplicate records, mismatched member IDs, enrollment timing gaps, and reporting lags from carriers or TPAs.
- We use it across underwriting, operations, clinical programs, and member experience to build an insurer that becomes more efficient as the technology improves.
Requirements
- 3-5 years in a data science or quantitative analyst role
- Proficiency in Python (scikit-learn, pandas, statsmodels) and SQL
- Comfort working with messy, inconsistent real-world datasets
- Experience designing or auditing data pipelines for quality and consistency
- Ability to communicate model behavior and business impact clearly to non-technical stakeholders
Nice to have
- Experience with GLMs, survival analysis, or credibility theory for pricing
- Exposure to sales funnel analytics or conversion attribution
- Familiarity with MLflow, dbt, or similar tooling
- You'll be an individual contributor embedded with underwriting, pricing, and sales teams - with direct access to the people who use your outputs daily.
- Work spans Python-based modeling and SQL-driven analysis equally
- Your models directly influence pricing decisions and sales outcomes, so accuracy, explainability, and sound uncertainty quantification are all first-class concerns.
- You'll work closely with engineering on data ingestion questions - this role requires comfort sitting at the boundary between data science and data engineering.
- High ownership: You'll get real responsibility from day one-our high-trust team empowers you to run with big problems and shape core parts of the company.
Skills
- Consistency monitoring.
Compensation
- Exact compensation inclusive of salary and any bonuses is determined based on a number of factors including experience and skill level, location, and qualifications which are assessed during the interview process.
Benefits
- Instead of optimizing ads or cutting labor costs, you'll use AI to fundamentally reimagine how people get healthcare.
Company info
- You'll get real responsibility from day one-our high-trust team empowers you to run with big problems and shape core parts of the company.
This listing is sourced directly from Arlo's careers page and normalized into a canonical job model.