Data Analyst II

Data Analyst II

Requirements

• Three to five years of experience in performing data management and/or statistical tasks. Graduate training can substitute for years of professional experience. Relevant internship or research experience is eligible.
• Fluency in R and/or Python for data science tasks.
• Proven experience successfully completing data deliverables, such as data pipelines and analytical output (e.g., reports, visualizations, dashboards)
• Ability to create custom data visualizations and interpret results.
• Proficiency in core statistical concepts including: standard errors, hypothesis testing, bias-variance tradeoffs, and regression modeling.
• Strong attention to detail and commitment to accuracy.
• Willingness to learn and contribute to a collaborative team environment.

Responsibilities

• Use scripting languages, such as R, Python, and SQL, to build data pipelines to create analytical datasets. Utilize data management and engineering tools and best practices to the extent feasible.
• Design and perform data quality control into pipelines, ensuring results of data quality processes are documented and transparent. Take ownership of the accuracy and quality of your work.
• Perform data analysis, including descriptive statistics and at times more sophisticated statistical analyses. Go above and beyond not just to deliver output, but ensure team members understand the results, implications, and assumptions. Written, verbal, and visual communication are all key to success in this role.
• Develop effective dashboards and reports in which the material produced significantly enhances storytelling, understanding, and interpretation of trends and patterns of business importance.
• Collaborate across a multi-functional team to collectively define requirements (analytical plans, definition of key metrics, output format) of the work and ensure the resulting work meets the needs of end-users.
• Engage with team expertise and healthcare quality subject matter to strategize how to improve our key metrics definition, data strategy, analyses plans, and the implementation of each of these.
• Manage multiple projects and collaborate with multiple teams simultaneously, effectively managing priorities. A related need is to accurately estimate the time needed to address data needs and deliver results by deadlines. Work to deliver results on time and keep team members abreast of progress and delays.
• Steward data security throughout all aspects of our projects by use of procedures and best in concordance with our data governance policies’ legal and ethical requirements.

Preferred Qualifications

• Experience with SQL or more database technologies, big-data methodologies and cloud methodologies is preferred. Understanding of relational database concepts is essential.
• Skills to develop reproducible reports using tools like R Markdown, Quarto, or Jupyter Notebooks.
• Proficiency in development of interactive dashboards using tools such as PowerBI or Shiny.
• Use of Git or other version control software for code development and collaboration.
• Data engineering and software engineering skills such as automation, data pipeline orchestration, data modeling, use of Databricks and related tools, etc. are valued.
• Mastery of core statistical concepts as well as knowledge of quality measure risk-adjustment, reliability, validity, and machine learning.
• Bringing growth mindset, enthusiasm, and compassion to your work and interactions with others
• Experience with healthcare domain is a plus, including knowledge of EHR and/or claims data, as well as health data standards and terminologies such as CQL, HL7.