Director, Data Science

Found in: Resume Library US A2 - 7 days ago

Secaucus New Jersey, United States Quest Diagnostics Full time
Overview:
Healthcare Analytics Solutions (HAS) is an innovative team within Quest Diagnostics that leverages Quest data to develop products and services to improve outcomes in healthcare across many different markets (Pharma, Clinical Trials, Health Plans/Payers, Hospitals/Health Systems, and Public Health agencies).

The Director, Data Science is responsible for leading advanced analytics in support of the Healthcare Analytics Solutions product portfolio. In this role, the Director will leverage their experience preparing large datasets and applying a variety of data science methods to analyze data, develop insights, and deploy analytics services and products. The Director will also be responsible for using the spectrum of data science fields of study, including descriptive, diagnostic, and predictive analytics, to address the biggest challenges facing each market.

 

The Director must be a subject matter expert in their respective domain. Prior experience and knowledge of data and analytics within the healthcare industry are critical to the role. This includes a proficiency in accumulating disparate data sources into structured forms to facilitate predictive analytics. The successful candidate will also support training and mentoring of other HAS data scientists in advancing their analytics capabilities.

Responsibilities:
Work with a cross-functional team of analysts, medical doctors, business leaders, and IT professionals to develop and deliver informatics and analytics products and services utilizing data from the Quest Diagnostics lab data and a variety of healthcare related data sources.

Lead the curation of data sets from Quest and non-Quest data sources in support of deriving business insights driven by advanced analytics. Ensures data sets are of high quality and integrity for the target analytics.

Lead the development, evaluation, and deployment of machine learning models including classification, regression, and forecasting for use in products and services.

Lead on-time and high-quality delivery of client/customer facing data and analytics components of HAS products.

Establish production machine learning capabilities such as performance tracking, drift monitoring, and bias detection.

Integrates advanced analytics and machine learning models into business products and services such as batch reporting, business intelligence dashboards, and real-time analytics.

Lead the creation of standard operating procedures and technology patterns within the HAS data science team.

Leverage data visualization tools and software to present large amounts of information in charts, graphs, or other diagrams that are easy to interpret and spot patterns, trends, and correlations.

Use unsupervised learning techniques including clustering and anomaly detection to uncover insights within healthcare data sets.

Lead the delivery of HAS analytics initiatives with high-level and ambiguous goals to advance the analytics capabilities of HAS.

Support the creation of customer contracts as related to data & analytics.

Represent HAS data science as a Subject Matter Expert in enterprise projects that enable the use of data for analytics across Quest. This requires collaborating with business and technical teams to propose solutions and/or leverage new technologies to advance data and analytics capabilities.

Builds and presents training to other data scientists related to advanced analytics topics such as machine learning algorithms, analytics technology, and best practices to grow team knowledge.

Develop trusted relationships with data and analytics counterparts with clients and customers.

Effectively summarize and communicate results, in written and presentation formats, from analysis to a diverse set of audiences with varying backgrounds and technical skills.

Develops experimental designs to support the evaluation of pilot products and features.

Support onboarding and training of new analytics team members, including setting up onboarding benchmarks to help grow team capacity.

Qualifications:
A Master’s degree from an accredited college or university in a related area of Data Science, Statistics, Computer Science, Mathematics, Economics, or Information Technology.

Analytics Certifications such as AWS SageMaker, SAS, or DataBricks.

10+ years of experience performing data analysis, preferably data mining, pattern recognition, statistical, and/or mathematical programming and modeling to very large health-related structured and unstructured data sets such as lab test data, claims data, and prescription data.

10+ years of professional analytics programming experience, preferably in the healthcare industry, using SQL, SAS, and Python to query data, prepare cleaned data sets, perform exploratory analysis, develop analytics models, and deploy artifacts for integration with products and services.

5+ years of experience incorporating machine learning concepts such as feature engineering, data sampling, drift detection, data leakage, and model explainability into analytics results.

2+ years of experience building and deploying predictive analytics models into production processes & products.

2+ years of experience in developing analytics solutions in a cloud environment using modern data science tools such as AWS, Azure, or GCP.

Experience with healthcare industry applications of predictive analytics for business problems. Examples include patient journey analysis, condition diagnosis and progression modeling, EHR analysis, clinical decision support, and population health trends.

Experience using interactive data visualization and BI tools such as Tableau, AWS QuickSight, or Microsoft PowerBI.

Familiarity and experience working with Agile methodologies.

Experience supporting the creation of SOWs and Contracts.

Expertise in Predictive Analytics algorithms and associated technology.

Expertise in data curation techniques and data quality evaluation methodology.

Expertise in feature engineering techniques.

Expertise in analytics-oriented languages, frameworks, and software such as SAS, Python, and SQL.

Demonstrable experience with big data analytics, preferably in cloud technologies such as Snowflake, AWS, GCP, and Azure.

Advanced knowledge of the latest machine learning and data visualization tools and methods as applied in business contexts.

Exceptional communication skills and executive presence; comfortable interacting and presenting with senior executives.

Superb relationship-building capabilities.

Strong ability to work on multiple projects simultaneously, balancing priorities, and work on cross-functional teams.

Travel requirement:

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