A permanent contract with the leader in innovation and high-tech engineering consulting.
A multi-stage model with career opportunities through specialization prospects with over 250'000 consultants around the world and a Group revenue CapGemini of EUR. 17 Bn, the Group is the undisputed global leader in Engineering and R&D services (ER&D).
In Switzerland, CapGemini employs more than 400 consultants and aims towards large growth with offices located in Zürich, Basel, Lausanne and Geneva.
Analysis of company and consortium data for different real-world digital mobility outcomes such as walking speed, for different pathological populations using wearable inertial devices (IMU, accelerometer and gyroscope
Building machine learning and statistical models for times series datasets and extracting clinically relevant features
Advise on current and future digital studies, staying up-to-date with current advanced data analytics both in standard statistics and machine learning
Hands-on analysis of study data following internal project management guidelines
Participate in or lead non-clinical activities
Maintain efficient interfaces with internal and external clinical teams including the global clinical statistics team, data integration engineers and analysts
Participate in the selection of CROs, vendors and technologies, as required, and supervise QSI project activities performed by CRO.
Program according to good coding practices and ensure internal quality control and reproducibility of deliverables.
Maintain records for all assigned projects and archive trail / project analysis and associated documentation. May involve designing or enhancing data storage system
Train staff on trial and project level activities and internal processes
Help review statistical analysis plans (SAP), summary reports and presentations for accuracy and clarity
MS, PhD, or equivalent, in mathematics, engineering, statistics or computer science
3+ years of relevant professional experience
Demonstrable experience working with time series data analysis
Demonstrable experience modelling, integrating and comparing a wide range of clinical data types (objective monitoring and assessments, subjective questionnaires, etc.) and sampling rates (daily, monthly, irregular)
Demonstrable expert knowledge of the R or python programming language and good programming practice (code annotation, version control, etc.)
Demonstrable leadership in driving scientific analytical plans and methods development.
Demonstrable ability in driving effective communication with cross-functional scientific/clinical teams