Job Location: Princeton, NJ
Job Detail:
Job Summary
The Associate Director role in Medical and Real World Analytics is for an expert in computational data analysis, mathematical modeling using both classical and modern methodologies from machine learning, complex systems, sensor data analysis, Engineering (except detailed software engineering). This expert will have a fundamental background (accomplishments demonstrated through knowledge and original work published in peer-reviewed avenues) to quickly pick up new areas or oversee work of others in new areas through a wide set of analytical skills.
You will engage with and influence partners through data-driven insights to progress Otsuka strategic direction. Develop outcomes-oriented relationships to acquire and deliver data and analytics products. Serve as lead for one or more compounds/TAs by understanding all aspects of compound lifecycle from data, analytics, insights, perspective and partner with internal DnA groups to deliver seamless value. Work with members of Medical and Real World Analytics and with different pillars in Data and Analytics.
When an opportunity is provided, the Associate Director will lead a team of data scientists with diverse technical backgrounds. Responsibilities will include resource management and mentoring team members on technical and cultural aspects towards their holistic growth aligned. Responsibilities will also include directly or indirect responsibility (through team members) for deliver from vendors.
While technical skills are required, this role also requires a strategic focus in delivering the goals of Med and Real World Data Analytics. In this capacity, the Associate Director will work with Head of Medical and Real World Data Analytics on implementing organization strategy to support operational/routine data and analytics requests and create, formulated, and implement high impact initiatives to organize (internal/external/Real World Data) and analyze data to convert into sound evidence for demonstrate value and difference of Otsuka products. This individual will help evolve Real World data collection from both classical and digital modalities.
Job Description
- Work with data programmers, statisticians, and partners including Medical Affairs, Value and Real World Evidence, and Market Access to support data products, insights, and ensure evidence generation through data science, mathematical and other forms of quantitative modeling.
- Lead projects/analyses by participating in data science analyses across the portfolio. Provide strategic inputs on advanced analytics related sections of protocols, analyses plans for Medical Affairs or Real World Data studies, data collection, exploratory analyses and publications.
- Lead identification of data gaps and recommend strategies to fill those gaps by suggesting new avenues for collecting during studies or applying external data assets while complying with regulations.
- Ability to formulate practical problems to deliver outcomes using ML and AI capabilities.
- Ensure delivery of quality and results by vendors and their resources, when such responsibilities are assigned directly or to the members in the team.
- Lead the planning and delivery of high quality data science solutions across a variety of data types ranging from clinical, and RWD (claims, sensor data, texts, labs, imaging, EMRs/EHRs, and others) forms of Real World Data.
- Lead development and implementation of efficient, modular, reusable data science solutions and collaborate to have those solutions implement those solutions. Contribute to scalable data science solutions and analytics platforms.
- Lead areas of data and advanced analytics strategy for assigned products for Medical and Real World Data Analytics, Global Medical Affair, and Global Value Real World Evidence.
- Leadership with active participation with oversight of creating datasets by defining patient or disease cohorts, pooling data across datasets to ensure rapid exploratory analyses, experimentation, and well-defined analyses.
- Establish and lead external collaborations with Academia and Industry research labs.
- Ensure professional development to enhance knowledge, skills, communication, scientific and technical methodologies, operational efficiency and compliance with policies and Regulations.
- Readiness to take responsibility to lead or build a data science team. Mentor team members to grow them to deliver a high performance. Responsible for continuous development of team and keeping them abreast and skilled with evolving methodologies.
Qualifications/ Required
Knowledge/ Experience and Skills:
- 5+ years hands-on experience in advanced data sciences and applying that to problems in pharmaceuticals, esp. digital medicine, personalization, behavioral biomarkers.
- Demonstrated leadership in real world data assets and applying them in CNS or Nephrology to produce scientific or value evidence.
- Experience using recent techniques and develop new methodologies.
- Demonstrated experience applying Machine Learning to at least one of the areas: healthcare data, clinical data, Bio-marker identification and related analysis, NLP/text analytics, social network analysis, sensor data, digital health, claims data, and Digital Signal Processing.
- Expertise based on deep hands-on experience with key data science packages (Python: Pytorch, TensorFlow, Keras, ML packages in AWS) to develop prototypes, develop algorithms specifically in clinical and real world data.
- Ability to deliver high impact and data science capabilities.
- Expertise in organizing, retrieving, searching, processing, mapping, and manipulating data in ML/AI pipelines.
- Ability to process and analyze diverse data, including text/documents,
- Familiarity to Python, R, SAS and other data processing language.
Educational Qualifications:
- PhD in a quantitative field such as Computer Science, Mathematics, Engineering, Physics, Statistics, or a related field with focus on advanced and modern Data Science, including the use of machine learning.
- Postdoc with research into novel methodologies or their application in pharmaceuticals is preferred.
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