Mba jobs in Germany

POSITION SUMMARY:
Reporting to the Director, Decision Support, Analytics, Data Science & Coding, and working as a member of the Division of Strategic Transformation, Quality & Data Analytics team, the Data Scientist plays a critical role in providing internal and external stakeholders with insights necessary for evaluating organizational performance and providing exemplary patient care.

The Data Scientist is responsible for advising the St. Joseph’s Healthcare Hamilton (SJHH) clinical and business leadership on the potential of data, to provide new insights into the organization’s strategy, and through the use of advanced statistical analysis, data mining, and data visualization techniques, to create solutions that enable enhanced performance.
The Data Scientist also plays a leading role in the management of a number of projects in support of the key stakeholder groups, where he/she is required to leverage and synthesize large volumes and variety of data in order to enhance the business’s understanding of individual population segments, propensities, outcomes, and decision points.
The Data Scientist combines data, computational science, and technology with strong knowledge of SJHH business and clinical processes, to drive high-value insights.

QUALIFICATIONS:

• Master’s degree in Computer Science, Mathematics & Statistical Science, MBA, Engineering preferred.
• Undergraduate Degree required
• 5 to 7 Years of work experience in the healthcare sector
• 5 to 7 years of experience combining data, computational science, and technology to drive high-value insights
• Clinical background an asset.
• Demonstrated ability to prioritize work activities of the project team and reallocate team resources as required to meet plan deliverables
• Sound SQL and Database knowledge and experience working on queries and reports, and/or sound working knowledge of applications/systems that support client groups, in such areas as Health Care, Pharmaceutical, Finance, Human Resources, Telecommunications, etc., are required.
• Must be certified in at least 3 of the following Epic 2018 certifications:
  • RPT150 – Reporting Workbench Fundamentals
  • RPT250 – Reporting Workbench Administration
  • RDR100 – Radar
  • RPT300 – Cogito Project Manager
  • RPT100 – Introduction to Crystal Reporting
  • CLR110 – Clarity Data Model Fundamentals
  • CLR130 – Clarity SQL Fundamentals
  • CLR202 – Clarity Data Model - Cadence
  • CLR201 – Clarity Data Model - Grand Central and Prelude
  • CLR205 – Clarity Data Model - EpicCare Ambulatory
  • CLR230 – Shared Clinical Clarity Data Model
  • CLR209 – Clarity Data Model - EpicCare Inpatient
  • CLR212 – Clarity Data Model – ASAP
  • CLR211 – Clarity Data Model - Health Information Management
  • CLR301 – Clarity Data Model - Resolute Hospital Billing

RESPONSIBILITIES:

• Develop and implement standards for identifying, cleaning and modeling data; Consult with the Clinical Applications team and Clinical/Operational decision makers to promote quality data collection build and workflow.
• The Data Science role requires a moderate degree of planning of work and a high degree of prioritization. The nature of planning is related to planning stakeholder involvement and automation of work tasks. The nature of the prioritization is related to organizational need and urgency.
• Support the development of data project budgets, which are brought forward for review to the Director, Decision Support, Analytics, Data Science & Coding
• Prioritize work items as defined by the strategic direction of the organization and impact, as defined by the information technology infrastructure library (ITIL).
• Advise on complexities and particulars that will impact cost and implementation dollars and advises on operational needs for the data project.
• Identify valuable data sources and automate collection processes
• Undertake preprocessing of structured and unstructured data
• Analyze large amounts of information to discover trends and patterns
• Build predictive models and machine-learning algorithms
• Combine models through ensemble modeling
• Present information using data visualization techniques
• Define key roles and stakeholders across the organization; i.e. data owners and stewards; and work with these stakeholders to develop data standards, policies and KPIs for data measurement
• Identify people, processes and technology required to streamline the execution of data-related projects