Data Scientist (Predictive Analytics/ Machine Learning)
We are looking for a creative, innovative and intellectually curious Data Scientist to join our Real World Evidence (RWE) Healthcare Analytics Center of Excellence (CoE) in London.
This is an exciting opportunity to work in one of the world's leading RWE teams working with Real World Evidence to help our clients answer specific questions globally, make more informed decisions and deliver results.
Our Predictive Analytics team is a fast growing group of collaborative, enthusiastic, and entrepreneurial individuals! In our never-ending quest for opportunities to harness the value of RWE, we are at the center of IMS Health’s advances in areas such as machine learning and cutting-edge statistical approaches. Our efforts improve retrospective clinical studies, under-diagnosis of rare diseases, personalized treatment response profiles, disease progression predictions, and clinical decision-support tools.
You will join this high profile team to work on ground-breaking problems in health outcomes across disease areas including Ophthalmology, Oncology, Neurology, Chronic diseases such as diabetes, and a variety of very rare conditions. The Predictive Analytics team work hand-in-hand with statisticians, epidemiologists and disease area experts across the wider global RWES team, leveraging a vast variety of anonymous patient-level information from sources such as electronic health records. The data encompasses IMS access to over 500 million anonymised patients as well as bespoke, custom partnerships with healthcare providers and payers.
You will play an important part in designing and delivering statistical / machine learning studies and predictive analytics solutions in a range of challenging analytical areas relating to patient health.
The role encompasses design and delivery of statistical and machine learning studies and solutions in addition to client business development.
Ideally you will have:
- Experience managing multiple statistical / machine learning projects in academia or commercial sector end to end with proven delivery capability including capturing requirements, designing analysis plans, interfacing with clients and report / manuscript writing.
- Excellent knowledge of supervised machine learning methods, such as regularized regressions, ensemble tree classifiers (e.g. Random Forests), Support Vector Machines, Neural Networks, etc.
- Strong programming skills in languages such as Python, R, C++ and Matlab and/or SPARK and Scala.
- Good grasp of classical statistical methods, such as fitting regression models, inference testing and sampling.
- Excellent written and spoken communication skills, including ability to present technical concepts to lay audiences, write analysis plans for projects, contribute to proposals / grant applications, pitch ideas effectively and persuasively to clients / internal stakeholders, etc.
- A proactive, innovative and pragmatic approach to problem solving and an ability to think critically and independently.
- Flexible and adaptable in a client focused, results driven environment.
- Comfortable and capable of liaising directly with senior stakeholders (internally and externally).
- MSC degree or higher involving machine learning
- Peer-reviewed publications involving machine learning
- Good knowledge of epidemiology / biostatistics, particularly analytical issues relating to studies of treatment effectiveness, disease progression, adherence, healthcare utilization, etc.
- Good knowledge of healthcare / life science issues involving Real-World Evidence.
- Experience with patient-level, longitudinal data.
We look forward to hearing from you!
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