Data Scientist (Biomedical) - R&D Data CoE - GSK
- Data Scientist
- Data Engineer
The use of data and analytics across the R&D organization has become a critical imperative in the transformation of the R&D process. The rapid growth of both internal and external data in conjunction with the creation of new modalities has increased the need for GSK to establish improved mechanisms in the collection, integration, use and re-use of data. The R&D Data CoE was established as a catalyst for the transformation to a more data-driven organization. This new organization has the focus to support all areas of R&D in the expanded use of data and analytics.
The Data CoE is comprised of four main teams, including the Data Science & Solutions team. This team provides direct support to areas of R&D in the creation of new applications of analytics. In addition to defining context for scientific and business questions, this team will develop and deliver analytics interfaces, portals, and toolkits to R&D to support decision making.
Key Responsibilities ...
The Data Scientist will be responsible for building models and applying statistical analytic approaches to a number of R&D challenges using large-scale, distributed systems in both relational and Hadoop environments. The Data Scientist will build, test and validate predictive/statistical models, leverage machine learning approaches, and use an array of analytics tools including Python, R, SQL, SAS and visualization tools.
The Data CoE is seeking a Biomedical Data Scientist with clinical experience. This expert will support drug development, computational and platform teams in addressing our most complex problems in drug discovery (genome analytics, safety prediction, target validation, biological networks) and increase the skills and capabilities of the broader Data Science community at GSK. Demonstrated deep experience in a clinical setting is required.
- Work with R&D teams on designing, building and deploying data analytical systems for large and very large datasets
- Support computational and platform teams in addressing our most complex problems in drug discovery (genome analytics, compound screening, safety prediction, target validation, biological networks)
- Design, develop and implement incubation analytical solutions using a variety of commercial and open source tools (e.g. R, SAS, Python, etc)
- Create algorithms to extract information from large and very large data sets
- Establish scalable, efficient, automated processes for model development, model validation and model implementation and large scale data analysis
- Develop metrics and prototypes to be used to drive business decisions
- Provide thought-leadership to the broader R&D organization on the optimal evaluation and implementation of machine learning approaches
- Identify emergent trends and opportunities for additional data analytical development.
Application of Knowledge ...
Delivery of key role responsibilities described in the Knowledge and Experiences sections involve:
- In partnership with scientific domain experts, develop and implement state-of-the-art Machine Learning approaches on novel R&D problems
- Continue to advance the Machine Learning discipline by evaluating unique aspects of drug discovery problems and configuring algorithms to provide a competitive advantage in:
- Disease understanding
- Biomarker development
- Target validation
- Speed to develop viable compound
- Prediction of compound success in humans (Efficacy/Safety)
Problem Solving & Innovation
This role will be operating in a complex working environment with multiple stakeholders, competing priorities and limited resources. This will require the jobholder to demonstrate vision and develop and manage a strategic plan that focuses on identifying and mitigating key Data Governance risks, using knowledge and judgment to ensure that optimal solutions are developed on time, with quality and within budget when appropriate.
Uses enterprise thinking and business acumen to ensure that the solutions have clear plans in place for delivery, implementation and embedding. This will involve applying these techniques to problem solving in areas where the job holder may have little detailed knowledge and/or expertise.
- Advanced degree in medical field (medicine, nursing, science) and/or Computer Science, Mathematics, Engineering, or related field
- Experience in a clinical setting;
- Experience with clinical data analytics; either clinical trial or “real world evidence” data; experience with ‘omics analytics preferred
- Proven direct experience in the use of analytical tools, such as SPSS, R, Python, etc to develop models;
- A deep knowledge of data analytics and the deployment of solutions within a diverse environment;
- Hands-on experience building retrospective and predictive models including clustering, classification, social network analysis, association to solve business issues;
- Proven ability to influence, communicate and negotiate with all levels of management across functional and business unit boundaries (i.e., Managers through R&D Leadership Team members);
- Excellent oral & written communication and presentation skills, with ability to communicate confidently and customize based on level of use and need to build strong relationships and networks across the organization.
- MD and advanced degree in Computer Science, Mathematics, Engineering
Key Capabilities Include
- Technical Expertise
- Insightful analytics
- Project Management
- R&D and industry knowledge
This role requires interaction with process owners across Pharma R&D teams. Success will only be achieved through the influencing of these process owners. In order to successfully influence these stakeholders, and deliver on the roles expectations and responsibilities, a strong set of executive communication skills will be required.
Excellent networking skills are required to ensure strong connectivity with the business and key stakeholders.
An ability to lead diverse teams across R&D is critical, particularly virtual teams using technology to support interactions
Teamworking is essential: to manage and coordinate the team towards effective delivery, have a project leadership approach that inspires confidence, buy-in and followership, and strengthens the team by encouraging active learning to develop the successful plans and deliverables.
Have a ‘can do’ attitude, helping the organization to develop new ways of working, flex between approaches and deal with ambiguity while directing others. Operate as a coach to other members of the groups operating in the Data arena.
Successfully delivering incubation projects across the Pharma R&D environment to drive new adoption of data and analytics is critical.
The impact of this work is significant from an operational and strategic standpoint:
it is broad reaching and includes the following: GSK’s scientific innovation agenda, patent protection, data exchange security, key business decisions, product progression, patient safety, public disclosures, patient-level data transparency and privacy, submissions to regulatory authorities, payers or other external parties, thereby leading to a loss of public trust and significant legal and financial penalties.
Closing date for applications: 3rd May 2017
When applying for this role, please use the 'cover letter' of the on-line application to clearly describe how you meet the competencies for this role, as outlined in the job requirements above. The information that you have provided in your cover letter will be used to assess your application.
Thank you for your interest in this opportunity.
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