Clinical Data Scientist

RedSail Technologies•Marshall Township, PA

1d•Hybrid

About The Position

The RedSail Technologies Network Services Business Unit has the primary mission to create incremental value streams for RedSail through the development and activation of Clinical, Financial, & Operational Programs that leverage our uniquely integrated technology platforms as well as our associated reach within the targeted market segments. The Clinical Data Scientist, will participate in the making use of RedSail’s data to evaluate and establish various impactful programs to accomplish and assist with the measurement of program effectiveness against the department objectives.

Requirements

Pharma/Pharmacy/Healthcare experience.
Experience programming with SQL scripting.
Experience with data analytics visualization tools such as PowerBI and Tableau.
Ability to transform complex data across multiple platforms into concise datasets.
Ability to visualize data in the most effective way possible for a given project or study.
Strong analytical and problem-solving skills; inquisitive.
Ability to work independently and with team members from different backgrounds and collaborative styles.
Excellent attention to detail with critical thinking skills.

Nice To Haves

A combination of both Doctor of Pharmacy and degree in Data Science, Data Engineering, or similar data relevant computer science/software development degree (i.e. Pharmacist Data Scientist) strongly preferred.
2 years of experience as a Data Analyst, Data Scientist or Data Engineer.
Experience with Pharma/Pharmacy transactions.
Experience programming with Python or Go Lang.

Responsibilities

Gathering data from various sources, such as databases, APIs, web scraping, and more. This can involve collecting structured and unstructured data.
Preprocessing the collected data to handle missing values, remove duplicates, correct inconsistencies, and address outliers. This step ensures data quality and reliability.
Analyzing the main characteristics of the data often through visualization and summary statistics. This helps in understanding data distributions, relationships between variables, and identifying patterns or anomalies.
Modifying data into a suitable format for analysis, such as normalization, standardization, or creating new features (feature engineering).
Creating visual representations of data, such as graphs, charts, and dashboards, to communicate findings effectively. Tools like Matplotlib, Seaborn, and Tableau are commonly used.
Applying statistical methods to understand data distributions, test hypotheses, and infer relationships. This can include t-tests, chi-square tests, ANOVA, and regression analysis.
Presenting findings, insights, and recommendations to stakeholders through reports, presentations, and storytelling. Effective communication is crucial for decision-making.
Working with professionals from various fields to ensure that the data science approach aligns with business goals and that the results are meaningful and actionable.
Continuously learning and adapting to new tools, technologies, and methodologies in data science to stay current and effective in the field.
Ensuring that data usage complies with ethical standards and legal regulations, such as data privacy laws (e.g., HIPAA).