Data Scientist

About the role

The Senior Applied Machine Learning Scientist will lead the development and deployment of predictive models that support critical business workflows. This role involves designing and maintaining machine learning models, enhancing model performance, and integrating them into operational systems.

Responsibilities

• Predictive Analytics & Decision Support
• Create and maintain machine learning models to assess opportunities and generate actionable recommendations.
• Develop predictive solutions to identify patterns associated with favorable outcomes, elevated risk, or operational priorities.
• Build classification, ranking, and scoring frameworks using historical performance and outcome data.
• Enhance model performance through the integration of internal and external datasets.
• Deliver model outputs through APIs and operational systems for direct consumption within business workflows.
• Intelligent Automation
• Collaborate with business and operational stakeholders to convert decision criteria into scalable data-driven solutions.
• Create feature engineering pipelines to transform raw information into machine-learning-ready datasets.
• Develop systems to improve consistency in handling complex evaluation processes while reducing manual effort.
• Implement explanation mechanisms to help users understand the primary factors influencing model predictions.
• Production Machine Learning & Platform Development
• Manage the complete ML lifecycle, including development, testing, deployment, monitoring, and ongoing optimization.
• Work closely with engineering teams to deploy models into production environments.
• Establish monitoring, alerting, and performance tracking processes to identify model degradation and data drift.
• Maintain model governance practices, including version control, reproducibility, and auditability.
• Contribute reusable components, workflows, and shared infrastructure to support broader data science initiatives.
• Document intelligence and information extraction solutions.

Requirements

• 3+ years of experience in Data Science, Applied Machine Learning, AI Engineering, or a related field.
• Demonstrated success deploying machine learning solutions into production environments used by business teams or customers.
• Strong Python development skills with emphasis on maintainability, testing, code quality, and software engineering best practices.
• Hands-on experience with machine learning libraries and frameworks such as scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow.
• Experience developing predictive models using real-world datasets, including classification, regression, ranking, and feature engineering techniques.
• Familiarity with data pipeline development and maintenance within production ecosystems.
• Experience with API development, containerization technologies, and cloud-based deployment architectures.
• Advanced SQL skills and experience working with modern cloud-based analytical databases and warehouses.
• Ability to translate ambiguous business challenges into measurable machine learning problems and practical technical solutions.

Preferred Qualifications

• Experience supporting large-scale operational decisioning environments where accuracy, efficiency, and consistency are critical.
• Background in industries such as financial services, healthcare, logistics, retail, marketplaces, software, or other data-intensive sectors.
• Prior exposure to insurance, underwriting, risk analytics, claims operations, or insurtech environments.
• Practical experience with large language models (LLMs), document processing platforms, retrieval-based AI systems, or extraction workflows.
• Familiarity with MLOps tools and model lifecycle platforms such as MLflow, SageMaker, Vertex AI, Weights & Biases, or similar technologies.
• Experience implementing model interpretability techniques and communicating results to non-technical stakeholders.
• Knowledge of external data enrichment strategies, including geographic, demographic, firmographic, and third-party datasets.
• Understanding of responsible AI principles, model governance, regulatory considerations, and fairness assessments within automated decision systems.