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 Senior Director, Data Science for our Healthcare Analytics Solutions (HAS) business is responsible for leading the HAS Data Science team and managing innovation and implementation of advanced analytics solutions within the organization. The HAS organization strives to generate insights to help solve the challenges of today’s complex healthcare landscape and develops data-driven analytics solutions to support health plans, health systems, pharmaceutical companies, and clinical trials.

The Senior Director will be responsible for maturing the organization and expanding the team to support the growth of the HAS business. Additionally, the successful candidate will research and explore novel approaches to complex challenges and help design, develop, and deploy innovative solutions that use the spectrum of data science fields of study, including natural language processing, machine learning and AI technology. The Senior Director will be a hands-on leader overseeing a highly skilled team and will coordinate and collaborate across HAS and other Quest BUs and functional areas to ensure the synchronicity of data science solutions.

• Provide leadership and management oversight and direction to a team of highly skilled data scientists, data analysts, and data engineers.
• Expand and build the HAS data science team through role definition, recruitment, training and development activities that progress business objectives.
• Partner with the HAS leadership team to drive innovation and growth in support of the HAS business strategy.
• Coordinate and collaborate with HAS business leaders and product managers to assess the current and future data science needs of the organization and translate this understanding into practical, stable, innovative recommendations to facilitate strategic solutions and help drive innovation.
• Provide thought leadership on data science tools and techniques and collaborate with business leaders to bring new products to market.
• Establish consistent, repeatable processes to support product and revenue growth.
• Coordinate and collaborate with the Quest IT Organization (HTAS) including the analytics and architecture teams, as well as analytics professionals throughout the organization to improve the enterprise data science development environment and suite of tools.
• Promote and influence the development and introduction of industry standards.
• Test and ensure data science solutions yield high-quality, high-confidence results in accordance with Quest’s quality standards and expectations of excellence in delivery of client services and solutions.
• Clearly documents and communicates objectives, requirements, and designs at these different levels of abstraction to both technical and non-technical audiences.
• Quickly and reasonably estimates capital, schedule, and human resources costs of proposed solutions. Compares, contrasts, and prioritizes among alternatives approaches while assessing risk both quantitatively and qualitatively.
• Uses seasoned judgment to suggest approaches that optimize among customer needs, business constraints, and technological realities.
• Promote industry and academic partnerships to enhance the reputation and positioning of Quest Diagnostics.
• Support a culture of continuous improvement with a will to win.

QUALIFICATIONS

• Advanced degree in engineering, science, mathematics, or other field related to data science and advanced analytics. A Ph.D. is preferred.
• 12+ years of relevant experience in big data, analytics, machine learning, and predictive modeling preferred.
• Expert knowledge and experience developing analytics solutions in a cloud environment using modern data science tools, programming languages, and libraries (Cloud certification preferred).
• Knowledge of SAS (SAS Studio, SAS Enterprise Guide, SAS Enterprise Miner, Yiya).
• Expert knowledge of the latest machine learning and data visualization tools and methods as applied in business contexts.
• Experience with the Machine Learning Life Cycle with demonstrable experience having taken advanced analytics and machine learning projects, from problem formulation, to research and exploration, through development and successful deployment.
• Proven track record of leading high-performing data science teams.
• Successful application of advanced quantitative analyses and statistical modeling that positively impact business performance.
• Experience and expertise with probability and statistics, inclusive of machine learning, experimental design, and optimization.
• Experience in scripting languages and rapid prototyping skills; including but not limited to SQL, Python, Perl, Java, VB.
• Skill in statistical and modeling packages such as SAS, Statistica, Matlab, R, and other advanced analysis tools.
• Ability to effectively summarize results from analysis to a diverse set of audiences with varying background and technical skills.
• Experience with time-series data leveraging methods such as regression, classification, and survival analysis.
• Experience with Deep Learning and associated tools, such as TensorFlow and GPUs.
• Prior experience managing remote teams.
• The ability to Influence and communicate effectively with non-technical audiences including senior business executives and managers.
• Demonstrated leadership skills, project management skills, strong written and verbal skills, and organizational skills. Proven ability to drive process with indirect authority to project team members.
• Experience in full software life cycle development, primarily operating in agile delivery.
• Must have a solid understanding of information technology and information security.
• The ability to quickly grasp the technical implications of business processes, and ability to provide valuable insight into the perspectives of users, managers, developers, and other stakeholders.
• Agility to comfortably move between highly varying levels of abstraction, from business strategy, to IT strategy, to high-level technical design.
• Customer-first work ethic.

Physical and Mental Requirements:

• A solid background in data science, tools, vendor relationships, technologies, standards, etc.
• Ability to multi-task
• Analytical skills
• Ability to follow verbal or written instructions
• Thinking analytically
• Communication
• Using effective verbal communication
• Using effective written communication
• Handling stress & emotions
• Concentrating on tasks
• Making decisions
• Adjusting to change
• Examining/observing details
• Sitting for long periods at a time