Data Scientist

Position Summary

The Consultant - Data Science contributes to the sustainment and incremental improvement of production Data Science solutions. Under the guidance of senior team members, the role supports data preparation, model evaluation, retraining, testing, monitoring, and issue resolution. The Consultant applies sound analytical and software engineering practices, learns existing model workflows, and works collaboratively with US-based Data Scientists and partner engineering teams.

Key Responsibilities

• Implement assigned model enhancements, monitor production behavior, investigate failures and contribute code fixes under minimal technical guidance.
• Write, test, and debug production-quality Python and SQL for data preparation, model training, scoring, evaluation and automated workflows.
• Maintain direct technical contribution regardless of title: independently inspect code, reproduce issues, write and run tests, and verify model results before production release.
• Execute assigned data preparation, feature engineering, model training or retraining, scoring, and evaluation tasks using established patterns.
• Run validation and regression tests, compare results with approved baselines, and document findings and anomalies.
• Triage data and model issues by examining inputs, transformations, logs, metrics, and model outputs; escalate issues that require deeper investigation.
• Write readable, tested Python and SQL and follow repository, branching, documentation, and code-review standards.
• Support model monitoring, data-quality checks, recurring performance reports, and scheduled refresh activities.
• Help maintain experiment logs, configuration, model versions, datasets or data references, and operational runbooks.
• Collaborate with other Data Scientists, Data Engineering, and platform teams to understand dependencies and resolve routine issues.
• Participate in knowledge-transfer sessions and document existing pipelines, assumptions, and troubleshooting steps.
• Follow approved deployment, change-management, privacy, security, and responsible AI procedures.
• Identify opportunities to automate repetitive checks and improve the reliability of existing workflows.

Education & Experience

• Bachelors degree in computer science engineering, statistics, data science or a related field required; advanced degree preferred.
• 8-10 years of overall relevant experience, including 3+ years in data science or statistical modeling.
• Practical Python and SQL skills, including data manipulation, analysis, and debugging.
• Working knowledge of machine learning fundamentals, model evaluation, feature engineering, and data-quality checks.
• Experience contributing to existing data science workflows or supporting model refreshes and enhancements.
• Familiarity with Git, unit testing, reproducible code, and technical documentation.
• Ability to follow technical guidance, communicate status and blockers, and learn an established production environment.
• Exposure to cloud platforms and model monitoring tools preferred.

Knowledge, Skills, Abilities & Behaviors

• Python and SQL proficiency
• Data preparation and feature engineering
• Model evaluation and regression testing
• Basic model monitoring
• Debugging and documentation
• Collaboration and learning agility

Hands-on Technical Expectations (Mandatory)

• Strong, demonstrable Python and SQL programming; candidates must independently code, test, debug, and explain their implementation.
• Hands-on statistical learning and machine learning: feature engineering, leakage prevention, cross-validation, forecasting/backtesting, error analysis, explainability, and model selection.
• Production MLOps: reproducible experiments, model/version registry, CI/CD, batch or online inference, drift/data quality monitoring, rollback, and automated retraining.
• Direct ownership of model sustainment and small enhancements for healthcare operations use cases, including Timpani volume forecasting and Nurse Handoff where assigned.