Seeking a Data Scientist with a related Math/Statistics/Analytics or CS degree and 4 years relevant data analytics/science experience.
Responsibilities: The Data Scientist works within a team that provides burden of illness trends, prevalence, national estimates, patient characterization of treatment and timing, forecasting, competitive profiling, regional targeting, publications, clinical trial site identification and clinical and financial outcomes. Customer include pharmaceutical companies, policy-makers and internal BD stakeholders. The ideal candidate is a highly organized and task oriented individual with a desire to pursue a career in industry leading healthcare data analytics. The preferred candidate will possess a broad familiarity with data analysis and management skills.
- Leverage strong math skills and statistical knowledge to advanced data mining and data analysis activities
- Applies knowledge of visualization, programming languages and data analytics to develop ad hoc and re-occurring reports
- Build, validate, and assess performance of risk-adjustment models and/or predictive models.
- Complete written documentation and reports of results in the form of business reports, internal technology white papers, quality assurance and improvement activity reports and statistical system documentation.
- Devise new algorithmic approaches to solve difficult quantitative problems using large scale data sources.
- Adopt new tools and algorithms effectively to improve model performance.
- Experiment with new algorithms and analytical approaches and report on the impact to model performance.
- Create tools and processes to download data, parse it for relevant content, and store it in existing data management systems
- Collect, explore, evaluate and transform data from databases, flat files, spreadsheets, and other data sources.
- Conduct deep research & ad hoc analyses using analytical best practices on clinical data, ensure HIPAA compliance, and prepare various styles of reports and visualizations required by internal and external customers.
- Translate complex analytical and data related concepts into a simplified consumable manner. Can create statistical tests to prove value of new products or business hypotheses.
- Understand database architecture and design principles. Demonstrates basic knowledge of database design principles and data administration standards.
- Quickly interpret and adapt to logical data models using both relational and dimensional modeling techniques
- Design advanced reports from queried data using SQL, SSRS, Excel, PowerPoint, SAS, Python and/or R.
- Analyze data in Python, R, and/or SAS is a plus
- Define methodology and analytics approach as required for each specific data analysis project and document all assumptions.
- Apply knowledge and skills to a wide range of standard and non-standard situations, uses creativity to solve problems and improve processes.
- Work independently with minimal guidance while functioning as a team member in a multi-disciplinary matrix environment.
- Align work priorities with organizational goals, set customer expectations on scope and deliverable schedules.
- Use Tableau to develop analytic solutions for a variety of stakeholders
- Bachelor's (BS) in Computer Science, Statistics, Analytics, Applied Statistics, Applied Mathematics, or related field preferred or equivalent work experience, advanced degree preferred
- Minimum 3 years of experience in data science/analytics required
Skills and Knowledge:
- Strong math, statistics, and data mining backgrounds, experience with predictive analytics and statistical hypothesis testing
- Programming experience in Python or other statistical programming languages such as R
- 1-3 years of programming experience using SQL
- Strong Data mining skills and experience working with large datasets
- Strong analytical, problem solving, organizational, and planning skills
- Proficient PC skills; including Microsoft Office products, SQL Server
- Proven database experience with the ability to write ad-hoc queries and test accuracy of results
- Understand basic statistical principles including hypothesis testing and regression.
- Experience with a broad range of modeling techniques such as generalized linear regression models, decision tree models, survival analysis, multilevel modeling, feature extraction, and text mining.
- Familiarity with supervised and unsupervised machine learning techniques preferred.