Data Architect Role with Pharma Domain

Role : Data Architect Role with Pharma Domain

Client Full Address: LAKEWOOD, OH (Hybrid)

Hire Type (FTE/Contract): Both

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, clinicaltrials.gov

·       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