Director of Data Science

WELLBE INTRODUCTION

The WellBe care model is a Physician Led Advanced Practice clinician driven geriatric care (care of older adults) team focused on the care of the frail, poly-chronic, elderly Medicare Advantage patients.  This population is typically underserved and very challenged with access to care.   To address these problems, we have elected to bring the care to the patient, instead of trying to bring the patient to the care. Care is provided throughout the entire continuum of care – from chronic care and urgent care in the home, to hospital, to skilled nursing facility, to assisted living, to palliative care, to end of life care.  WellBe's physician/advanced practicing clinician led geriatric care teams’ partner with the patient’s primary care physician to provide concierge level geriatric medical care and social support in the home as well as delivering and coordinating across the entire care continuum.

GENERAL SUMMARY

As the Director of Data Science at WellBe Senior Medical, you will play a pivotal role in leading our data science team to harness the power of data and analytics to drive strategic initiatives and inform decision-making across the organization. You will be responsible for overseeing the development and implementation of advanced data analytics solutions that address complex challenges in healthcare delivery, clinical research, and operational efficiency.

SKILLS & COMPETENCIES 

• Team Management: Ability to build, lead, and motivate a team of data scientists, ensuring alignment with organizational goals and objectives.
• Strategic Vision: Develop and communicate a clear vision for the data science department, outlining strategic priorities, goals, and objectives.
• Decision-Making: Make informed decisions regarding project prioritization, resource allocation, and technology investments to maximize the impact of data science initiatives.
• Change Management: Guide the team through organizational changes and transitions, fostering a culture of adaptability and resilience.
• Advanced Analytics: Proficiency in applying advanced analytics techniques such as machine learning, predictive modeling, and deep learning to solve complex healthcare problems.
• Statistical Knowledge: Solid understanding of statistical methods and techniques for analyzing healthcare data, including hypothesis testing, regression analysis, and clustering algorithms.
• Data Visualization: Ability to create visually compelling and informative data visualizations to communicate analytical findings and insights to stakeholders effectively.
• Clinical Understanding: Familiarity with clinical terminology, healthcare processes, and workflows to contextualize data analysis and interpretation.
• Regulatory Knowledge: Understanding of healthcare regulations and compliance requirements, including HIPAA, HITECH, and GDPR, to ensure adherence to privacy and security standards.
• Industry Trends: Stay updated on emerging trends and developments in the healthcare industry, including value-based care, population health management, and healthcare interoperability.
• Programming Skills: Proficiency in programming languages such as Python, R, and SQL for data manipulation, analysis, and modeling tasks.
• Big Data Technologies: Familiarity with big data technologies and platforms such as Hadoop, Spark, and distributed computing frameworks for processing and analyzing large-scale healthcare datasets.
• Cloud Computing: Experience with cloud platforms such as AWS, Azure, or Google Cloud for storing, managing, and analyzing healthcare data in scalable and cost-effective ways.
• Business Acumen: Understanding of business objectives and key performance indicators (KPIs) to align data science initiatives with strategic priorities and drive business value.
• Problem-Solving Skills: Ability to identify strategic opportunities and challenges, analyze complex problems, and develop data-driven solutions to address them effectively.
• Risk Management: Assess risks and uncertainties associated with data science projects and develop mitigation strategies to minimize negative impacts and maximize project success.
• Effective Communication: Clear and concise communication skills to convey technical concepts, analytical findings, and recommendations to non-technical stakeholders in a comprehensible manner.
• Stakeholder Engagement: Engage with stakeholders at all levels of the organization, including executives, clinicians, administrators, and IT professionals, to understand their needs and priorities and advocate for data-driven decision-making.
• Storytelling: Ability to craft compelling narratives around data insights, using storytelling techniques to convey the relevance and significance of analytical findings to diverse audiences.
• Project Planning: Develop project plans, timelines, and milestones, defining scope, objectives, and deliverables in alignment with strategic goals.
• Resource Allocation: Allocate resources, including personnel, budget, and technology infrastructure, effectively to support data science projects and initiatives.
• Risk Assessment: Identify potential risks and dependencies, develop risk mitigation plans, and monitor project progress to ensure timely and successful completion.
• Data Governance: Establish data governance policies and procedures to ensure the ethical and responsible use of healthcare data, including data privacy, security, and confidentiality.
• Regulatory Compliance: Ensure compliance with healthcare regulations and standards, including HIPAA, HITECH, and GDPR, by implementing appropriate controls and safeguards for data handling and analysis.
• Ethical Considerations: Address ethical considerations related to data science, including informed consent, transparency, and fairness in data collection, analysis, and decision-making processes.
• Cross-Functional Collaboration: Collaborate effectively with multidisciplinary teams, including clinicians, researchers, IT professionals, and business stakeholders, to integrate data science into healthcare workflows and decision-making processes.
• Relationship Building: Build strong relationships and partnerships with internal and external stakeholders to foster collaboration, trust, and support for data science initiatives.
• Influencing Skills: Use influence and persuasion techniques to advocate for data-driven decision-making and promote a culture of data literacy and analytics adoption across the organization.
• Professional Development: Engage in continuous learning and professional development activities, including training, workshops, conferences, and networking events, to stay updated on the latest advancements in data science methodologies, technologies, and industry trends.
• Knowledge Sharing: Share knowledge, best practices, and lessons learned with colleagues and team members, fostering a culture of collaboration, innovation, and continuous improvement within the data science department and across the organization.
• Adaptability: Embrace change and adapt to evolving technologies, methodologies, and industry trends, demonstrating agility and flexibility in response to new challenges and opportunities in the healthcare landscape.

QUALIFICATIONS

Educational/Experience Requirements:

• Masters Required / Ph.D preferred
• 7+ years of data science experience required
• 7+ years of people management

 Required Skills and Abilities:

• Ability to lead, mentor, and inspire a team of data scientists, fostering a collaborative and high-performing work culture.
• Experience in setting strategic objectives, defining team priorities, and aligning data science initiatives with organizational goals.
• Strong communication and interpersonal skills to effectively delegate tasks, provide feedback, and resolve conflicts within the team.
• Proficiency in developing data science strategies that align with the company's overall business objectives and drive competitive advantage.
• Experience in prioritizing and sequencing data science projects based on their potential impact, resource requirements, and strategic importance.
• Ability to oversee the execution of data science initiatives, ensuring timely delivery of high-quality solutions that meet stakeholders' expectations.
• Deep understanding of data science methodologies, algorithms, and techniques, with hands-on experience in applying them to solve complex business problems.
• Proficiency in programming languages such as Python, R, or Scala, as well as familiarity with data manipulation and analysis libraries (e.g., Pandas, NumPy).
• Experience with machine learning frameworks (e.g., TensorFlow, PyTorch) and big data technologies (e.g., Hadoop, Spark) for processing and analyzing large datasets.
• Knowledge of data governance principles and best practices, including data privacy, security, and regulatory compliance requirements.
• Experience in implementing data governance frameworks, policies, and procedures to ensure the integrity, confidentiality, and availability of corporate data assets.
• Understanding of relevant regulations such as GDPR, HIPAA, or CCPA, and the ability to ensure compliance across data science initiatives.
• Strong business acumen with the ability to translate business objectives into data science projects and deliver actionable insights to drive decision-making.
• Experience in collaborating with cross-functional teams and building strong relationships with stakeholders across different departments (e.g., marketing, product, finance).
• Effective communication skills to convey complex technical concepts and analytical findings to non-technical audiences, including executives and senior management.
• Commitment to innovation and staying abreast of emerging trends, technologies, and best practices in the field of data science.
• Ability to drive a culture of continuous improvement within the data science team, encouraging experimentation, learning, and knowledge sharing.
• Experience in evaluating and adopting new tools, methodologies, and technologies to enhance the effectiveness and efficiency of data science initiatives.
• Proficiency in project management methodologies and tools for planning, executing, and monitoring data science projects.
• Experience in managing project timelines, budgets, and resources effectively to ensure successful project delivery.
• Strong problem-solving skills and the ability to overcome challenges and obstacles that may arise during project execution.
• Strong analytical and critical thinking skills to frame business problems, analyze complex datasets, and derive actionable insights.
• Experience in developing analytical models, algorithms, and statistical techniques to solve business problems and optimize decision-making processes.
• Ability to evaluate the effectiveness of data science solutions and iterate on them to improve performance and outcomes.
• Ability to foster a collaborative and inclusive team environment where team members feel empowered, motivated, and supported.
• Experience in mentoring and developing junior data scientists, providing guidance, feedback, and opportunities for growth and professional development.
• Commitment to building a diverse and inclusive team that leverages different perspectives and experiences to drive innovation and creativity.
• Adaptability to navigate and thrive in a dynamic and fast-paced work environment, where priorities and requirements may change rapidly.
• Resilience to overcome setbacks and challenges, maintaining focus and momentum towards achieving long-term goals and objectives.
• Ability to embrace change and lead the team with confidence and agility through periods of transition and transformation.

Supervisory Responsibility: Yes, supervisory responsibilities

Travel requirements: Travel may be required up to 10% locally or nationally

Work Conditions: Ability to lift up to 20lbs.  Moving lifting or transferring of patients may involve lifting of up to 50lbs as well as assist with weights of more than 100lbs. 

• Ability to stand for extended periods
• Ability to drive to patient locations (ie. home, hospital, SNF, etc)
• Fine motor skills
• Visual acuity

The preceding functions may not be comprehensive in scope regarding work performed by an employee assigned to this position classification.  Management reserves the right to add, modify, change or rescind the work assignments of this position.  Management also reserves the right to make reasonable accommodations so that a qualified employee(s) can perform the essential functions of this role.