Direct Hire | Healthcare | Philadelphia, PA | Apply Now –
ROLE SUMMARY
Reporting to the Global Chief Data and Analytics Officer and working as a key member of the leadership team, the VP, Data Science will be a key role in transforming how data-driven decision making drives business outcomes.
Business enablement fueled through analytics is a foundational enabler of our client’s strategy to provide personalized service direct-to-customer, drive affordability in the healthcare system, and support innovation. The VP, Data Science role is an opportunity for a proven leader in the field who can define and build out the advanced capabilities in predictive analytics, machine learning and AI that will lead to achievement of goals.
This role will work collaboratively with peers across Global Data & Analytics team as well as business stakeholders to build and deliver Data Science solutions that drive business and customer impact, while deepening and scaling internal capabilities and talent.
KEY ROLE OBJECTIVES and RESPONSIBILITIES
- Provide thought leadership and contribute to developing and executing our client’s Global Data & Analytics strategy and roadmap.
- Challenge the way our client does business through application of advanced analytics and data science including predictive modeling, machine learning, natural language processing, deep learning, pattern recognition, expert systems and AI.
- Lead and evolve Data Science capabilities and effective delivery within the Global Data & Analytics function.
- Work in close partnership with analytics and data engineering leaders and peers to design, build, and deploy data science solutions that create meaningful and measurable impact.
- Support the operationalization and integration of insights in business processes and collaborate with business facing analytics teams to ensure adoption and value realization.
- Define and execute data science talent strategy, provide individual and team leadership to data scientists to ensure they are growing their capabilities and achieving their career goals.
- Define and evolve data science methods, toolset and processes including the use of advanced and emerging methodologies, end-to-end analytic development lifecycle, and agile ways of working.
- Lead and oversee the development of analytic platform and enablers including the use and adoption of commercial and open source tools, common workflows, and deployment capabilities in partnership with Engineering/Technology leadership.
- Drive repeatability, reuse and scalability of data science solutions across the enterprise.
- Oversee data science and predictive model governance, performance monitoring, and controls.
- Blend creativity, relentless problem-solving, business acumen, and leadership to drive innovation through data science working with Innovation team.
- Lead and foster the data science community across the enterprise.
KEY QUALIFICATIONS and EXPERIENCE
- 10+ years industry-specific experience solving business problems through application of quantitative analytic approaches.
- Preferred experience in healthcare or consulting industries managing enabling functional teams.
- Minimum of a Master’s degree in quantitative field such as mathematics, statistics, operations research, or engineering/technology with an emphasis on statistical analysis and data science.
- Proven track record for success in leading high-performance data science teams and building out best-in-class capabilities at the enterprise level.
- Deep expertise in advanced analytics and data science methods, tools, platforms and processes required to successfully design, build and operationalize data science solutions.
- Combination of analytical, technical and business acumen.
- Knowledge of big data tools and platforms, structured and unstructured data mining, analysis of real-time/streaming data and usage of external and non-traditional data sources.
- Demonstrated thought leadership on challenging problems with examples of implementing innovative analytic/data science solutions and driving outstanding results.
- Proven ability to work collaboratively in complex matrix organizations and agile teams.
- Experience managing high degree of change and complexity across organization and portfolio.
- Self-motivated leader who can work under a high level of ambiguity.
- Effective leadership, prioritization and management of complex deliverable portfolio in a dynamic environment.
- Ability to prototype statistical analysis and modeling algorithms, and apply these algorithms for data driven solutions to problems in new domains.
