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Fully Remote Position

Description/Comment:

• The Danaher Diagnostics Sepsis initiative was formed in 2017 under the leadership of Danaher's CSO, with the aim to move the needle on sepsis and reduce mortality globally by bringing innovative technology solutions to market. The team is composed of individuals from the Danaher Diagnostic platform, Beckman Coulter, Cepheid, and Radiometer operating companies (OpCos) who work together collaboratively on sepsis solutions. By positioning the different Danaher operating companies' sepsis product lines together through a robust clinical evidence strategy, there is an exciting opportunity to make a greater impact in improving clinical outcomes for patients with sepsis.
• The Beckman Digital Dx Sepsis solution incorporates an AI/ML algorithm, trained on real world data (RWD) from patient medical records linked with hematology data generated by the Beckman Coulter DxH900 instrument (which includes the FDA cleared Early Sepsis Indicator parameter), to predict sepsis onset for a patient. Beckman Coulter has also partnered with the Biomedical Advanced Research and Development Authority (BARDA), under the Office of the Assistant Secretary for Preparedness and Response (ASPR), within the United States Department of Health and Human Services (HHS) to commercialize this Digital Dx Sepsis solution. The press release of this partnership can be found at https://www.beckmancoulter.com/en/about-beckman-coulter/newsroom/press-releases/2019/q4/2019-29-october-bec-receives-barda-funding
• Building from the success of the Sepsis initiative thus far, Danaher Diagnostics is creating a data analytics and science team to generate novel insights and real world evidence (RWE) from real world data (RWD) that contain lab results generated by DHR Dx OpCo instruments linked to patient medical records. In this role, you will be responsible for driving technical execution and and helping us create a best-in-class data & analytics organization. You will work with various stakeholders both inside and outside the organization to execute on our research initiatives.

Responsibilities

• Collaborate with stakeholders to understand data requirements for ML, Data Science and Analytics projects.
• Assemble large, complex data sets from disparate sources, writing code, scripts, and queries, as appropriate to efficiently extract, QC, clean, harmonize and visualize Big Data sets.
• Write pipelines for optimal extraction, transformation, and loading of data from a wide variety of data sources using Python, SQL, Spark, AWS, and Azure 'big data' technologies.
• Identify, design, and implement continuous process improvements such as automating manual processes and optimizing data delivery.
• Document data processes, write data management recommended procedures, and create training materials relating to data management best practices.

Required Qualifications

• Previous experience performing data engineering tasks on RWD/RWE projects involving Electronic Medical Records (EMR) data
• Experience with defining clinical protocols to collect RWD
• Fluency in Python with strong knowledge of standard data science toolkits
• Advanced SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases.
• Familiarity with healthcare data standards, data ontologies, toolchains, and operating procedures
• An associate who is independent, self-motivated, and eager to excel in a goal-oriented and multi-faceted work environment.
• Someone who embraces uncertainty and thrives by driving to clarity in a fast-paced ambiguous environment
• Excellent written and verbal communication skills and the ability to clearly articulate project goals, timelines, and key milestones, and accomplishments to stakeholders.

Additional Job Details:

• FULLY REMOTE. (Choice of US time zones: Easter, Central, Mountain, Pacific) Shift hours are typically 8 AM-5 PM. -------
• TOP 3 QUALIFICATIONS: 1. Experience with processing (quality control, normalization, harmonization) EMR/EHR data into analysis ready datasets 2. Experience to automate the data processing by building data processing pipelines (preferably via Python) 3. Understanding of common clinical data standards and ontologies (eg SNOMED, OMOP, CDISC, LOINC)