Quick Overview
Job Description
Role : Data Tech Lead with Pharma Domain
Client Full Address: LAKEWOOD 44107, OH
Must have : PubMed , Python , SQL • Oversee the completion of projects from internal kickoff, development, delivery, and continuing support
• Coordinating with other teams to ensure the scope of the project is correct/meeting client’s expectations
Required Skills:
· Programming: Proficiency in Python, R, and SQL.
· Math and Stats: Strong background in statistics, probability, and algebra.
· Tools: Experience with data frameworks, big data tools, and visualization software like Tableau or Power BI.
· Education: Bachelor’s or master’s degree in computer science, Math, Statistics, Engineering, or a related quantitative field.
Mandatory Skills:
· Knowledge / Good understanding of PubMed Data
· Experience as Disease management SME
· Experience in Scrum
· Some Alteryx & Snowflake experience
Familiarity with:
· PubMed, its data, MeSH structure
· Clinical trials data,
· Claims: Mx and Rx data, ICD10 codes, taxonomies, CPT, NDC (less important than PubMed)
· JIRA/Confluence
· Alteryx Gallery and Designer
· Tableau desktop, Curator
Role & Responsibilities:
· Oversee the completion of projects from internal kickoff, development, delivery, and continuing support
· Coordinating with other teams to ensure the scope of the project is correct/meeting client’s expectations
· Manage development and other teams
· Set up and run internal workflows and manage data as it is modified per project.
· Ensure project data is prepared and uploaded to client inface in a timely manner
· Maintain project refresh schedule
· Interface with vendors: order profile information, conference abstracts
· Troubleshoot data issues on a project-by-project basis
· Collaborate with Product department on potential updates to the deliverable
Looking for below Technical Domain Expertise under Health Care sector.
· Data Collection: Gather structured and unstructured data from internal and external sources.
· Data Cleaning: Process, verify, and clean data to ensure accuracy.
· Model Building: Design and train machine learning models and predictive algorithms.
· Analysis: Run exploratory data analysis to find hidden trends and patterns.
· Communication: Share findings with company leaders using clear reports and visual dashboards
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