Novacore

Data Scientist

• Build and maintain the models that evaluate inbound requests in real time — scoring quality, flagging risk signals, and routing items to the right handling path before a human reviews them.
• Train classification and ranking models on historical outcome data (accepted, declined, loss events) to predict account quality and prioritize review queues — similar in structure to fraud scoring at fintechs, patient risk stratification in healthcare, or lead scoring in high-volume sales platforms.
• Integrate structured and unstructured third-party data signals as model features: geospatial layers, firmographic data, external risk indicators, and document-extracted fields.
• Serve model outputs via API so scores and flags appear natively inside workflow tools used by the operations team — your model is a product feature, not a report.
• Translate business rules and decisioning criteria into machine-executable logic that can be applied programmatically at intake — moving decisions that currently require human judgment into automated or assisted pathways.
• Build and own the feature engineering pipelines that feed these models: normalizing inputs, handling missing data, encoding categorical variables, and enriching records with external data sources.
• Develop model explainability layers so end users understand why a record was scored or routed a particular way — a requirement for user trust and, in our industry, regulatory defensibility.
• Own the full deployment lifecycle: containerize models, write inference APIs, coordinate with engineering on production integration, and set up monitoring for model drift and performance degradation over time.
• Build pipelines to extract structured data from unstructured documents: forms, PDFs, emails, and attachments that arrive as part of the intake workflow. Apply NLP and LLM-based extraction techniques to reduce manual data entry and improve the completeness of records entering the decision workflow.

Applied Data Scientist