Data Scientist V, Biostatistics

Key Skills

Algorithms, Data Architecture, Data Engineering, Deep Learning, Knowledge graph, Machine learning techniques, Neural Networks, R Programming, SQL, Tableau

Job Description

In addition to the responsibilities listed below, this technical leader biostatistician is also responsible for driving the research process by serving as expert on grant proposals and scientific publications; leading the development of documentation to capture the processes and project workflows as they relate to data management and statistical methods; defining metrics to ensure data quality; and translating statistical and algorithmic models to aid in the drawing of conclusions about study populations.

Essential Responsibilities:

Promotes learning in others by communicating information and providing advice to drive projects forward; builds relationships with cross-functional stakeholders. Listens, responds to, seeks, and addresses performance feedback; provides actionable feedback to others, including upward feedback to leadership and mentors junior team members. Practices self-leadership; creates and executes plans to capitalize on strengths and improve opportunity areas; influences team members within assigned team or unit. Adapts to competing demands and new responsibilities; adapts to and learns from change, challenges, and feedback. Models team collaboration within and across teams.

Conducts or oversees business-specific projects by applying deep expertise in subject area; promotes adherence to all procedures and policies. Partners internally and externally to make effective business decisions; determines and carries out processes and methodologies; solves complex problems; escalates high-priority issues or risks, as appropriate; monitors progress and results. Develops work plans to meet business priorities and deadlines; coordinates and delegates resources to accomplish organizational goals. Recognizes and capitalizes on improvement opportunities; evaluates recommendations made; influences the completion of project tasks by others.

Leads the development of detailed problem statements outlining hypotheses and their effect on target clients/customers by ensuring comprehensive and accurate definitions of scope, objectives, outcome statements and metrics.

Leads the design and development of data pipelines and automation for data acquisition and ingestion of raw data from multiple data sources and data formats by overseeing the transformation, cleansing, and storing of data for consumption by downstream processes; writing and optimizing diverse and complex SQL queries; and demonstrating expertise of database fundamentals.

Serves as an expert in the analysis and investigation of complex data sets by ensuring optimum data visualization methods are employed; determining how best to manipulate data sources to discover patterns, spot anomalies, test hypotheses, and/or check assumptions; and reviewing and verifying summaries of key dataset characteristics.

Leads the selection, manipulation, and transformation of data into features used in machine learning algorithms by leveraging and demonstrating expertise in techniques to conduct dimensionality reduction, feature importance, and feature selection.

Trains statistical models by selecting and leveraging algorithms and data mining techniques; leading model testing by ensuring the proper use of various algorithms to assess the input dataset and related features; and applying techniques to prevent overfitting such as cross-validation.

Leads the deployment and maintenance of reliable and efficient models through production.

Verifies and ensures model performance by demonstrating advanced expertise in the practice of a variety of model validation techniques to assess and discriminate the goodness of model fit; and leveraging feedback and output to manage and strengthen model performance.

Partners with internal and external stakeholders across domains to develop and deliver statistical driven outcomes by generating and delivering insights and values from heterogeneous data to investigate complex problems for multiple use cases; driving informed decision-making; and presenting findings to both technical and non-technical leadership.

Minimum Qualifications:

• Minimum four (4) years medical or health analytics experience.
• Minimum five (5) years statistical modeling experience using SAS, R, or another advanced statistical package.
• Minimum three (3) years experience working with Exploratory Data Analysis (EDA) and visualization methods.
• Minimum five (5) years machine learning and/or algorithmic experience.
• Minimum five (5) years statistical analysis and modeling experience.
• Minimum five (5) years programming experience.
• Minimum three (3) years experience in a leadership role with or without direct reports.
• Bachelors degree in Mathematics, Statistics, Computer Science, Engineering, Economics, Public Health, or related field AND Minimum eight (8) years experience in data science or a directly related field. Additional equivalent work experience in a directly related field may be substituted for the degree requirement. Advanced degrees may be substituted for the work experience requirements.
• Masters degree in Biostatistics, Statistics, Public Health, Data Science, or related field OR Minimum five (5) years medical or health analytics experience.

Additional Requirements:

• Knowledge, Skills, and Abilities (KSAs): Health Care Coding; Internal or External Publication; Procedure Manuals and Documentation; Process Mapping; Strategic Thinking; Advanced Quantitative Data Modeling; Algorithms; Applied Data Analysis; Data Extraction; Data Visualization Tools; Deep Learning/Neural Networks; Machine Learning; Relational Database Management; Project Management; Microsoft Excel; Design Thinking; Business Intelligence Tools; Data Manipulation/Wrangling; Data Ensemble Techniques; Feature Analysis/Engineering; Open Source Languages & Tools; Model Optimization; Data Architecture; Data Engineering

Preferred Qualifications:

• One (1) year healthcare experience.
• One (1) year regulatory experience.
• One (1) or more publications as an author in a medical or scientific journal.
• Three (3) years experience delivering presentations to management.
• Three (3) years experience working in a matrixed organization.
• Two (2) years relational database experience.
• Four (4) years experience working with SQL.
• Four (4) years experience working with SAS.
• Three (3) years experience working with Excel.
• Four (4) years experience working with Tableau.
• Four (4) years study design experience.
• Three (3) years experience working with the design of experiments.
• Three (3) years experience working with causal inference.
• Two (2) years experience setting up data architectures.
• Two (2) years experience working in big data or data engineering.
• Four (4) years data wrangling experience.
• Master's degree in Mathematics, Statistics, Computer Science, Engineering, Economics, Public Health, or related field.