Data Scientist - Predictive Analytics / Machine Learning - (1721208)
Basic Job Info
Join us on our exciting journey! IQVIA™ is The Human Data Science Company™, focused on using data and science to help healthcare clients find better solutions for their patients. Formed through the merger of IMS Health and Quintiles, IQVIA offers a broad range of solutions that harness advances in healthcare information, technology, analytics and human ingenuity to drive healthcare forward.
DATA SCIENTIST (PREDICTIVE ANALYTICS/ MACHINE LEARNING)
We are looking for a creative, innovative and intellectually curious and entrepreneurial Data Scientist to join our Real World Insights (RWI) Healthcare Analytics Center of Excellence (CoE).
This is an exciting opportunity to work in one of the world's leading RWI teams working with large-scale anonymized patient data Insights 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 RWI, we are at the center of IQVIA'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 RWI team, leveraging a vast variety of anonymous patient-level information from sources such as electronic health records. The data encompasses IQVIA's access to over 530 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.
Ideally you will have:
• Experience of 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 Python and/or R. Experience in BORIS, SPARK or Scala is highly beneficial.
• Some experience with MySQL is beneficial.
• Solid understanding of best coding practices and version control software such as Git, ability to write clean and efficient code and a good understanding of the data science package landscape is essential. 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.
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