The
Senior Data Scientist will work on a team of data scientists and engineers to deliver data science solutions
He or she will be responsible for working on complex problems in the field of life-saving implantable medical devices He or she will use machine learning, artificial intelligence, statistical modeling, data mining, and visualization techniques to provide analytics solutions to a wide range of challenging projects As a key member of the team, the candidate will develop solutions to curate and analyze large quantities of medical data The candidate will work on projects that provide solutions to internal and external stakeholders The candidate will collaborate effectively with internal stakeholders and cross-functional teams in India and the US He or she will present solutions and insights in concise and effective manner to technical and non-technical audiences
About This Role In this Senior Data Scientist role, you are responsible for managing the acquisition, processing and analysis of non-Abbott Medical Devices sourced observational real-world data (RWD), including clinical, insurance claims, disease registry, electronic health/ medical records (EHR/EMR) and syndicate corporate internal metadata & data
you'll manage a wide portfolio of data types, with a goal of supporting diverse analyses across R&D You will ensure data transfers follow quality standards and coordinate with other stakeholders in MEDICAL DEVICES COMMERCIALS, SCIENTIFIC, CLINICAL, REGULATORY AND QUALITY TEAMS to ensure acquisitions satisfy expected analysis plans
In addition, you'll program analyses of the RWD, working with your observational data sciences colleagues, and stakeholders across Abbott Medical Devices, including epidemiologists, translational biologists, biomarker scientists and biostatisticians These analyses will provide a better understanding of disease natural history, incidence/prevalence, co-morbidities, treatment patterns, and health and safety outcomes in real world patient populations, across a wide variety of serious and unmet diseases Your work will have great impact to research and early development, clinical development pipeline and marketed products
What you'll Do As a Senior Data Scientist, you will:
- Conduct analyses and develop advance data science models pertaining to the application of the observational Rwe'data for diverse stakeholders using health outcomes focused algorithms and other observational studies to better understand disease natural history, incidence/prevalence, co-morbidities, treatment patterns, and health and safety outcomes in real world patient populations and their commercial usage
- Ensure data transfers follow quality standards and coordinate with other stakeholders in MEDICAL DEVICES COMMERCIAL, SCIENTIFIC, CLINICAL, REGULATORY AND QUALITY TEAMS to ensure acquisitions satisfy expected analysis plans
- Define and implement data transfer, quality control and curation processes, and maintain the data quality pipeline
- Work with other Global Data Sciences team members to ensure consistency of quality control methods and processes across disparate types of data sourced external to Abbott Medical Devices
- Advise on the associated analyses plans using information developed in the execution of the data quality pipelines
- Ensure information is surfaced to ensure the externally sourced data is fit for purpose
- Collaborate with external vendors, Abbott Medical Devices partners and external organizations responsible for collecting the data
- Develop systems, processes, and tools (pipelines) which impact enterprise use of our data assets and confirm data quality
Who you are You love analytics and are passionate about using observational real-world data to drive meaningful insight that inform the science that will defeat devastating cardiovascular & neurological diseases
You love learning new technological skills and collaborating with your team and stakeholders to make us leaders in the research and development of medicines to transform neuroscience to benefit society
Qualifications
Minimum Required Skills - A bachelors degree or Higher in Computer Science, Biostatistics, Data Science, Life Sciences, Epidemiology, Bioinformatics, Clinical Informatics, or similar technical fields
- 5+ Years of experience developing solution using Advance Analytics, Use of AI-ML Methods such as PCAs, Regression, Classification, Clustering, Decision Trees, Random Forest, Neural Networks and Deep Learning methods etc is required
- Knowledge of data exploration and feature engineering and trending and familiarity with statistics and be able to use Descriptive, Inferential, Predictive and Prescriptive Statistics models/methods to solve data problems
- Expertise in at least one of these Big-Data/Analytics coding languages (R, Python, SQL, Azure/Data Bricks etc)
- Review of the study report to ensure statistical integrity in the reporting of the results
- Interpret analysis and craft statistical sections of integrated report
- Experience using hardware and software used to curate, process and analyze data
- Strong Analytical skills, and ability to effectively lead, collaborate and communicate across diverse group of observational data stakeholders (scientific, technical, operational)
Preferred Desirable Skills - masters in Computer Science, Biostatistics, Data Science, Life Sciences, Epidemiology, Bioinformatics
PhDs can be considered with 3 years of experience - 6+ years relevant work experience with a focus on clinical, observational or Rwe'data analysis through ingestion, curation, exploration, mining, and insights generation and ability to work major portion of several typical data science project independently with minimal to no supervision
- Exposure to Medical devices, Cardiovascular, neurology/ neuroscience will be added on
- Experience establishing and managing relationships with external vendors and organizations for the acquisition and analysis using data visualization techniques
- Understanding of clinical development and life sciences and healthcare industry, with emphasis on role of observational research
- Experience developing and implementing process and data standards and develop SAP, Analysis Datasets, Mock Shells, TLFs and validate them to support analysis
