Commercial Data Analyst LOC;Durham, NC
100,000 - 200,000
Job Description:
Primary Responsibilities include but are not limited to:
- Work closely with internal clients to develop and deliver standard reports including trending, forecasting, correlation and regression models, and data mining for trends.
- Analyze data accuracy/relevance and ensure data integrity of reports from heterogeneous data sources.
- Integrate multiple data sources to produce a comprehensive view of the customer/product/market environment.
- Provide analytical support for key Hematology and Hypermunes initiatives such as sales force effectiveness, pilot success, strategic planning, ROI analyses, etc.
- Work with clients to translate business requirements into functional and technical specifications to be implemented in a standard report, ad hoc report, or BI dashboard.
- Proven ability to quickly learn business processes and identify business analysis opportunities.
- Develop and refine (as needed) Hematology & Hypermunes deliverables dynamic scorecards with key metrics such as historical and forecasted purchases, days on hand inventory, pull through, market share, etc.
- Assist in the evaluation and procurement of relevant third part data to support the Hematology & Hypermunes business unit - formulary, medical claims, account profiles, cost of care, etc.
- Coordinate with colleagues to prepare product-by-product reviews for all NA Commercial Operations meetings, to include ex-factory sales, pull through, and inventory/DOH trends
Requirements:
- At a minimum, the candidate will possess a Bacheloras Degree and a minimum of 8 years of work experience with 4 years in life sciences, preferred.
- The candidate should have a proven track record in an analytical capacity, consulting, and management reporting. A commercial analysis background with experience in pharmaceuticals/biotechnology is ideal.
- The ability to travel up to 10% should be expected. Experience with QlikView and Salesforce.com is preferred.
- Advanced training in data management practices and data modeling techniques is preferred for senior analyst level.