Dignity Health, one of the nation's largest health care systems, is a 22-state network of more than 9,000 physicians, 63,000 employees, and 400 care centers, including hospitals, urgent and occupational care, imaging and surgery centers, home health, and primary care clinics. Headquartered in San Francisco, Dignity Health is dedicated to providing compassionate, high-quality, and affordable patient-centered care with special attention to the poor and underserved. In FY17, Dignity Health provided $2.6 billion in charitable care and community services. For more information, please visit our website at www.dignityhealth.org . You can also follow us on Twitter and Facebook.
Junior Data Scientists are responsible for conducting data analysis and developing moderately complex designs algorithms, identifying opportunities through the use of statistical, algorithmic, mining and visualization techniques. In addition to advanced analytic skills, this role is also proficient at integrating and preparing large, varied datasets, architecting specialized database and computing environments, and communicating results.
Jr. Data Scientists work closely with clients, data stewards, project/program managers, and other IT teams to turn data into critical information and knowledge that can be used to make sound organizational decisions.
Nature and Scope:
This position reports to the Medical Director of Health Analytics. This position does not have direct supervisory responsibility
Primary Responsibilities: developing and maintaining good communications and working relationships with the medical staff, administrative staff, outside agencies and with other departments.
Duty to Support the Mission and Philosophy of Dignity Health:
This position upholds the best interests of Dignity Health by adhering to the highest standards of ethical and moral conduct and by committing to the values of Dignity Health's mission and philosophy.
Resources are dedicated to: delivering compassionate, high-quality, affordable health services; serving and advocating for our sisters and brothers who are poor and disenfranchised; and collaborating with others in the community to improve the quality of life.
Design, extract, normalize, analyze, review and automate analysis for internal audit utilizing enterprise data warehouse, extracts and data mining tools; coordinate with business for outside data source requirements.
Design, develop, maintain and communicate visual dashboards.
Design and develop ad-hoc analysis based on business requirement needs.
Identify and use appropriate investigative and analytical technologies to interpret and verify results.
Coordinate with business to ensure follow-up and resolution of exceptions including specific individual resolution as well as root-cause analysis and control gap identification.
Review large software implementations to identify transaction flow gaps, design flaws and data integrity issues.
Maintain databases and related programs in a thorough and efficient manner.
Use best practices, with limited coaching, to develop statistical, machine learning techniques to build models that address business needs.
Utilize effective project planning techniques to break down basic and occasionally moderately complex projects into tasks and ensure deadlines are kept.
Use and learns a wide variety of tools and languages to achieve results (e.g., R, SAS, Python, Hadoop).
Collaborates with the team in order to improve the effectiveness of business decisions through the use of data and machine learning/predictive modeling.
Innovates on projects by using new modeling techniques or tools.
Contributes on a wide variety of projects.
Executes on modeling/machine learning projects effectively.
Communicates findings to team and leadership ensure models are well understood and incorporated into business processes.
Works with leaders to ensure the project will meet their needs.
Reviews and evaluates on appropriateness of techniques, given current modeling practices, to senior leadership.
Take part in training courses to increase skill set and technical capabilities in order to better serve the needs of the business.
Advanced degree in Applied Statistics, Economics, Computer Science, or Operations Research
Master's or PhD preferred in a quantitative field such as statistics, mathematics, computer science, finance, or economics.
1+ years' experience in advanced analytics, model building, statistical modeling, optimization, and machine learning algorithms including supervised and unsupervised learning, boosting and ensemble methods.
Experience in test engineering for information technology
Experience designing, developing, implementing and maintaining a database and programs to manage data analysis efforts.
1 year experience with data mining and tools (i.e. Bayesian analysis, SPSS, SAS, R, JMP, RapidMiner, NLP, Python, SQL, NoSQL, Java, C, etc.).
Experience / Strong Knowledge of Regression/Linear Analysis
Experience with cloud computing and Hadoop (MapReduce, PIG, HIVE)
Experience with third-party API integration.
Experience with Spark.
Experience with graph databases.
Experience working with SAP preferred
Experience in normalizing data to ensure it is homogeneous and consistently formatted to enable sorting, query and analysis.
Knowledge, Skills and Abilities:
Familiarity with any of Deep Learning, Distributed RF, Generalized Linear Model, K-Mean and naive Bayes
Ability to aggregate, normalize and process data by authoring predictive algorithms to synthesize and present actionable data insights.
Technical mastery in one or more of the following languages/tools to wrangle and understand data: Python (NumPy, SciPy, scikit-learn), R, Matlab, Spotfire, Tableau.
Ability to interpret business requests as well as communicate findings in a user-friendly manner.
Ability to complete projects with evidence of creative and critical thinking a must.
Understanding of Data Warehousing is a must.
Proficient in the use of Teradata SQL, MS SQL server (SSIS/SSAS experience preferred), Data Visualization (e.g., Tableau or other), MS Access, MS Excel, Visual Basic, and SharePoint.
Working knowledge of building self-serve analytics tools for business users a plus.
Working knowledge of statistical analysis, data mining and predictive modeling tools and techniques a plus.
Working knowledge of application development and/or web development a plus.
An understanding of risk management methodology and factors.
Strong business analytical skills a must; ability to apply business logic to design and implement data mining techniques on large data sets.
Ability to write clear, concise reports and presentations with an ability to orally communicate effectively; organizational and documentation skills a must.
Demonstrated ability to work independently and within a team in a fast changing environment with changing priorities and changing time constraints.
A strong passion for empirical research and for answering hard questions with data
Strong interpersonal skills and ability to multi-task.