Principal Consultant, Advanced Analytics: Data Science and AI

Purpose Of This Role
The Principal Consultant, Advanced Analytics: Data Science and AI contributes statistical capabilities and methodological leadership at all stages of projects, from planning to completion. The role includes working with junior team members in designing, developing, and delivering client solutions across multiple projects—leveraging competencies in statistical theory, data analysis and interpretation, regression analysis, machine learning algorithm development, deep learning, and natural language processing techniques. Additionally, the role is critical in driving business development for datascience solutions across the clinical development and market access spectrum. The individual must have a Master’s or Doctoral degree in Health Economics, Statistics, Biostatistics, Mathematics, or another quantitative field. They should be proficient in machine learning techniques including targeted learning as well as statistical software/tools such as R, Stata, Python, and SAS.
Knowledge and Experience You will require for this role

Strong basis in fundamental statistical concepts and methods and familiarity with techniques such as development of predictive equations, survival analysis (including parametric methods), longitudinal data analysis, meta-analysis, mixed treatment comparison, and other hierarchical analysis techniques, etc.
Familiarity with machine learning techniques and Bayesian statistics is a plus.
Strong statistical programming skills with standard software, including SAS, R, or STATA.
Strong communication (spoken and written) and problem‑solving skills, and an ability to learn‑quickly.
Ability to communicate effectively, in non‑technical terms, with project team members.
Ability to work well in a team as well as independently and be able to take leadership role with regard to methodological elements in projects.

Education Requirements

MSc or PhD in Data Science, Medical Statistics, Computational Biology, Health Economics, Health Policy, Statistics, Biostatistics, Mathematics or other quantitative fields.

Skills, Competencies and Capabilities you will need to qualify for this role

Six or more years of working experience with healthcare consulting or pharmaceutical organizations.
Familiarity with machine learning use cases in HEOR including the prediction of risk of various health care events; the causal estimation of treatment effects; developing models for economic evaluation; and model/data transparency.
Willingness to work under pressure to meet multiple and sometimes competing deadlines.
Excellent scientific, business writing, and presentation skills with close attention to detail.
Exceptional communication skills, especially in the relaying of technical information and project concepts.
Competent in written and spoken English.
SAS (Base, Stat, Graph, Macro), R, SPSS, STATA, and Python.

Key Accountabilities Of This Role Include

To direct project teams in the design, development and delivery of client solutions across multiple projects.
To provide high level input to the development of client deliverables including the provision of support to the delivery team in the development of strategic recommendations tailored to individual projects.
To provide advice and support to existing clients both within and outside of projects.
To help manage existing business accounts and identify new business opportunities for Parexel with existing and new clients.
To proactively mentor and develop members of the team to help achieve best in class status.
To ensure the optimal levels of client management are maintained at all levels and that the training and support to achieve best practice consultancy standards are achieved.
To ensure quality standards are adhered to on all projects and new methodologies and techniques are adequately assessed and implemented.
To foster thought leadership opportunities in Advanced Analytics.
To work with Senior Management colleagues to identify further service opportunities.

Additional Responsibilities
Principal Consultant, Advanced Analytics: Data Science and AI is responsible for ensuring that all assigned projects are conducted in an efficient manner and that quality and client satisfaction is maximized at all times – ensuring the direction of the project and the quality of the deliverables meet the project objectives and the client needs. Further, Principal Consultants are expected to support and train the Senior associates and Associates in their daily duties and to flag any areas of acute training needs to their line managers. Supported by the senior staff and Business Development partners, the Principal Consultant is responsible for maintaining client relationships on their projects and driving new business development (with a sales target). In addition, the individual will be expected to contribute to the continuing growth and improvement of the business unit through taking ownership of company and business unit processes and initiatives as well as contributing to the Senior Management Team's focus and direction. The Principal Consultant will also be expected to meet stated targets for new business development.
Skills
Candidates will be part of multi-disciplinary research teams and will be expected to provide statistical expertise and methodological leadership at all stages of projects from planning to completion. Duties will vary according to the nature of the projects. These may include independently contributing to the preparation of study protocols, data manipulation and analysis, development of machine learning algorithms, application of deep learning and natural language processing techniques, and assisting with the interpretation and dissemination of findings. Candidate is expected to also lead and support ongoing innovation objectives of the unit in the field of health outcomes analysis which warrants having thought leadership skills. Candidate is expected to also support ongoing thought leadership and innovation objectives of the unit in the field of advanced analytics including, but not limited to:

Supervised and unsupervised learning
Variations in machine learning algorithm development such as regression, classification, clustering, and dimensionality reduction
Variations of ensemble methods such as boosting, bagging, and stacking improve model performance
Deep learning
Super learners
Targeted learning
Targeted maximum likelihood estimation
Target trial emulation and other causal inference applications
Causal modelling
Predictive modelling
Feature engineering
Natural language processing
Large language models

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