Data Scientist

Description Hybrid onsite Tuesday Wednesday and Thursday

As a Data Scientist / Machine Learning Engineer on our AI Builder program, you will design, develop, and operationalize advanced analytics and machine learning solutions focused on anomaly detection, pattern recognition, predictive modeling, and intelligent monitoring across large, complex healthcare and operational datasets. You’ll identify meaningful patterns, emerging signals, and behavioral shifts that inform enterprise decisions and improve operational processes.

This role also plays an important part in moving data science solutions from experimentation into production. You’ll collaborate with engineering, architecture, and business teams to build reliable pipelines, automated model workflows, and integrations between analytical solutions and enterprise systems — applying statistical rigor and modern MLOps practices in equal measure.

• Design, develop, and maintain anomaly detection and pattern recognition systems across large-scale healthcare and operational datasets, using techniques such as clustering, classification, time-series analysis, change-point detection, and graph-based analytics.
• Develop reusable feature engineering, scoring, and analytical components that support multiple enterprise use cases rather than isolated point solutions.
• Apply natural language processing, large language models, and other machine-learning techniques to unstructured and semi-structured data to surface patterns, themes, and emerging signals.
• Design and contribute to production-grade machine learning pipelines, including automated data preparation, feature generation, training, validation, deployment, scoring, and monitoring.
• Develop and maintain CI/CD workflows for data science solutions, including source control, automated testing, model versioning, and rollback capabilities.
• Establish monitoring for production analytical systems — model performance, data quality, feature drift, model drift, and pipeline health.
• Partner with engineering and technology teams to integrate models and services with enterprise applications, APIs, and downstream business processes.
• Communicate analytical findings, model behavior, and limitations clearly to both technical and non-technical stakeholders.

The successful candidate can independently solve complex analytical problems and move solutions beyond exploratory analysis into reliable, integrated production systems. You have a strong foundation in statistics and machine learning, with genuine interest in anomaly detection, pattern recognition, and finding meaningful signal in large, messy datasets. You understand that good data science requires more than model development, and you’re comfortable partnering with engineers on deployment, automation, and operational support.

• Strong professional experience in Data Science, Machine Learning, advanced analytics, statistical modeling, or a related discipline.
• Strong hands-on programming capability in Python.
• Strong SQL skills and experience working with large relational or analytical datasets.
• Strong foundation in statistics, machine learning, model evaluation, and experimental design.
• Experience developing real-world models using techniques such as classification, clustering, anomaly detection, predictive modeling, time-series analysis, or related approaches.
• Experience with data preparation, feature engineering, target construction, validation, and model performance evaluation.
• Experience developing reusable and maintainable analytical code rather than exclusively notebook-based or ad hoc analysis.
• Experience helping move machine-learning or…