The Data Scientist I, Advanced Methods role is a Commercial Research position contributing to the development of Oracle Life Science’s commercial offer, harnessing health databases, healthcare expertise, and knowledge of the commercialization needs of the life sciences industry.  The role works directly with Senior/Client Leads, Research to determine the best methods and approaches to answer business questions utilizing real world data sources and primary market research.  

What you can expect from working with Oracle Life Sciences

A passionate and growing quantitative network comprising expertise in advanced methods, behavioral science, electronic health records, and digital methods. A stead-fast and growth strategy focused on oncology, rare disease, and specialist healthcare / life-science Work within a team (immediate and global) culture of growth-mindset and collaboration, focused on inclusivity, idea sharing and fun A development framework, encompassing all levels of the career journey, to enhance knowledge and skills in this ever-evolving industry

 

Responsibilities:

Designs, develops and programs methods, processes, and systems to consolidate and analyze unstructured, diverse “big data” sources to generate actionable insights and solutions for client services and product enhancement.  Also supports advanced analytics for primary market research.

This position is critical to identifying insights from health data to answer life sciences commercial business questions and improve patient outcomes.

Primary Responsibilities

Enable development of commercial syndicated and semi-syndicated offers from real world data Provide internal and client support and education regarding health data sets Support custom commercial business development discussions regarding real world data and participate in the creation of custom commercial proposals Support execution and completion of custom projects involving real world data

 Technical Responsibilities

Access, extract, cleanse, compile and transform disparate data sets to conduct exploratory and pre-defined analyses using scientifically valid techniques to determine data quality and generate meaningful insights Contribute to discovery of meaningful insights from the data that relate to important use-cases using scientifically valid techniques Support iterative selection and application of modern statistical and machine learning techniques and evaluation methods to derive best candidate approaches and models Interact with clinical and business teams and leaders to identify relevant questions and issues for data analysis and experimentation that support client clinical and business needs or problems. Propose new uses for existing data sets or sources, algorithms, and predictive models

Qualifications

Basic Qualifications

Education: Masters (Ph.D. preferred) in Mathematics, Statistics, Economics, Information Systems, Information Technologies, Management Information Systems, Computer Sciences, Public Health, or Epidemiology or related area of study

Minimum of 3+ years industry experience in data science work including data management, transformation, and visualization

At least 3 years of work experience with SQL, Python, or PySpark

Proficient in communicating effectively with both technical and nontechnical stakeholders

Experience building complex data sets from multiple data sources, both internally and externally

Ability to manipulate large data sets with high dimensionality and complexity; fluency in SQL (or other database languages) and a scripting language (Python or R), experience with machine learning

 

Preferred Qualifications

At least 1 year of work experience with Cloud technologies, like AWS, Azure, or OCI

At least 1 year of work experience with Big Data tools, like Databricks, Jupyter Notebooks

Advanced competency and expertise in Python, Databricks, and other platforms

Advanced competency with unstructured data and NLP techniques

Strong background working with predictive and statistical modeling, machine learning and strong expertise in all phases of the modeling pipeline