Sr Spclst, Decision Science, Data Science

Job Description

Our IT team operates as a business partner proposing ideas and innovative solutions that enable new organizational capabilities. Our team is a diverse collection of scientists and engineers working towards the same goal – enabling and accelerating the next generation of pharmaceutical sciences. We collaborate closely with laboratory and in-silico scientists, and other IT colleagues on the value chain to propose and implement innovative solutions that enable new organizational capabilities.

Decision Science is seated within the Data Science and Scientific Informatics (DSSI) team of Research and Development Sciences IT (RaDS-IT. We are responsible for developing and applying techniques for quantitative cost-risk-benefit analysis of complex scenarios in R&D. We deeply engage with stakeholders to understand challenges in real-world processes, extract domain knowledge, and design solutions. Our solution process involves mathematical modeling of data and processes implemented through efficient and modular computational tools to enhance business intelligence and decision-making capability for our stakeholders.

A successful application will engage with stakeholders to understand and formalize real-world problems; will apply data and/or process modeling techniques to represent problems in computation, use methods of dynamical systems, Bayesian inference, simulation, uncertainty quantification and AI/ML; and will be responsible for sustained, meaningful engagement with stakeholders to iteratively refine problem understanding/formalization and present results; work with other IT product teams to discover, develop, and deploy products and tools, and work with Business Technology Partners to handle shifting priorities. The position will collaborate with scientists, IT product teams, and engineers across the entire breadth of the pharmaceutical pipeline, from discovery to clinical assessment. The position presents an opportunity to apply cutting-edge mathematical and computational techniques to an emerging field with exciting and impactful applications in drug discovery and development.

Education

• M.A./M.S. or PhD in Applied Mathematics, Computer Science, Operations Research/Industrial Engineering, Data Science, Bioinformatics/Informatics, Statistics, Computational Biology, Physics, or can demonstrate equivalence qualifications in related fields with some experience in the above domains.

Qualifications

• Theoretical knowledge and practical experience in some of the following domains: constrained optimization, database design, dynamical systems, machine learning, Bayesian statistical modeling, logic programming.

• Experience in applied mathematics or informatics in a medical, healthcare, public health, and/or pharmaceutical domain.

• Fluency in at least one programming language commonly used in scientific domains, e.g., Julia, Python, MATLAB/Octave, R

• Experience interacting with subject matter experts and stakeholders outside the domain of applicant’s expertise to deliver reports or solutions.

• Demonstrated ability to engage scientists in many fields, and a willingness to acquire necessary domain knowledge.

• Manage project schedule, communicate with stakeholders, and deliver results on a timely basis with a high level of independence and discipline.

• Provide guidance and mentorship for junior employees and collaborators within and outside the organization.

• Identify opportunities for methodological innovations and execute on real problem areas.

• Excellent team player and coordination

• Excellent verbal and written communication skills

Preferred

• Experience with the Julia programming language, and relevant package ecosystems (SciML, AlgebraicJulia, Turing, etc.)

• Experience in data/knowledge representation and reasoning, e.g., ontologies, SQL/NoSQL, knowledge graphs/OWL, Datalog.

• Experience with process modeling, e.g., Petri nets, discrete event simulation, Resource Task Networks, Business Process Modeling Notation.

• Experience in high performance computing, GPU computing.

• Publication history in related fields.

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