Applied Data Scientist

🔥 0 minutes ago

Apply Now

Find Similar Remote Jobs

Receive Emails For Remote Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Novacore

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 1990

🤝 B2B

⚕️ Healthcare Insurance

💸 Finance

B2B • Healthcare Insurance • Finance

Novacore is a specialty commercial insurance platform and managing general agent (MGA/MGU) that delivers tailored programs and underwriting solutions for complex and hard-to-place risks across sectors including healthcare, real estate, transportation, social services, reinsurance, and diversified B2B. Backed by a large network of agent partners and substantial premium volume, Novacore combines proven underwriting expertise with advanced technology and real-time analytics to accelerate quoting, improve operational efficiency, and support growth for agents and carriers.

📋 Description

• 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

• 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

• 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