Applied Data Scientist

Novacore

Applied Data Scientist at Novacore developing classification models and AI infrastructure for specialty insurance. Collaborating to improve real-time decision-making in insurance workflows.

Posted 6/26/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

Amazon RedshiftAWSAzureBigQueryCloudDockerFlaskGoogle Cloud PlatformPythonPyTorchScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

3+ years of experience as a data scientist or ML engineer, with several production deployments where your model ran inside a system used by real end users.
Strong Python skills with production-grade coding practices: modular, tested, version-controlled code — not just notebook-quality work.
Hands-on experience with ML frameworks (scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow) and applied knowledge of classification, ranking, regression, and feature engineering for real-world, noisy datasets.
Experience building and maintaining data pipelines that feed production models — scheduled, monitored, and reliable, not just ad hoc EDA scripts.
Familiarity with model deployment patterns: REST APIs (FastAPI or Flask), containerization (Docker), and cloud deployment on AWS, GCP, or Azure.
Proficient in SQL; comfortable pulling and transforming data from a cloud warehouse (Snowflake, BigQuery, or Redshift) as part of feature engineering workflows.
Strong problem-framing instincts: you can take an ambiguous business problem, identify whether ML is the right tool, define the target variable, and scope the modeling approach before writing a line of code.

Benefits

Comp & perks

A collaborative, results-driven environment
Competitive compensation and comprehensive benefits
Year-round social and community events
Ongoing mentorship and professional development
Endless opportunities for upward mobility

ATS Keywords

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Hard Skills & Tools

Pythonmachine learningfeature engineeringclassificationrankingregressionNLPLLM-based extractionmodel explainabilitydata pipeline development

Soft Skills

problem framingcommunicationcollaborationcritical thinkingdecision making