The Sr. Principal Data Scientist will transform large data sets to business valueby analyzing, deriving insights, and identifying opportunities for product and process optimizations. The ideal candidate must have strong experience using a variety of data mining and analysis methods, using a variety of data tools, building, implementing, and validating models. The candidate will work closely with other data engineers, as well as a with a wide range of stakeholders and functional teams.
Principal Duties & Responsibilities:

• Work directly with data engineers, data analysts, architects, and business stakeholders to identify opportunities and use cases to solve real world challenges using Advanced Analytics models.
• Design, build and maintain a scalable automated infrastructure for extraction, transformation, and aggregation of data from various data sources.
• Use data mining to extract information from data sets and identify interesting/unusual patterns, predict usage trends and system performance.
• Apply structured approaches to leveraging large data sets to uncover new insights.
• Lead, coach, and mentor other data scientists, machine learning and data engineers.
• Communicate, interpret, and explain modeling output to stakeholders

Education:
Ph.D. in a quantitative field or MS with equivalent experience in computer science, engineering, or information technology.
Minimum years of work experience: 10+ years of experience working in professional business environments.
Required Qualifications/ Experience:

• 5+ years of data science experience with a background in rigorous data-driven inference methods (such as statistics, data mining, machine learning, and sound experimental design).
• Proficient at working with and manipulating large data sets, using modern big-data systems (e.g., Azure storage/compute) and scientific tools (e.g., Python, Spark, or Scala packages).
• Deep experience with using the Python data science stack; including pandas, scikit-learn, NumPy, XGBoost, notebook environments, etc.
• Proven experience applying descriptive, predictive, and prescriptive statistics to real-world problems.
• Experience with using Platform tools (such as Azure ML, DataRobot, AWS SageMaker.
• Demonstrated leadership in Data Science overall workflow and methodology supporting the need to move systems into production and support.
• Experience with managing disparate data sources, including preprocessing, cleansing, and verifying data integrity.

Preferred Qualifications/ Experience

• Experience in data prep with industry standard tools like R, Python, etc. and with Visualization tools (PowerBI Tableau, etc.)
• Experience in operationalization of data science including MLOps, sustainable development lifecycle.
• Experience working with various types of healthcare data including claims, electronic medical records, encounters, patient safety data, survey data, enrollment and/or provider data.

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