Life Sciences Analytics Specialist

PharmaACE is a growing Global Healthcare Consulting Firm, headquartered in Princeton, New Jersey. Our expert teams of Business Analysts, based across the US, Canada, Europe, and India, provide Analytics and Business Solutions using our worldwide delivery models for a wide range of clients. Our clients include established, multinational BioPharma leaders and innovators, as well as entrepreneurial firms on the cutting edge of science. We have deep expertise in Forecasting, Business Analytics, Competitive Intelligence, Sales Analytics, and the Analytics Centre of Excellence Model. Our wealth of therapeutic area experience cuts across Oncology, Immuno- science, CNS, CV-Met, and Rare Diseases. We support our clients' needs in Primary Care, Specialty Care, and Hospital business units, and we have managed portfolios in the Biologics space, Branded Pharmaceuticals, Generics, APIs, Diagnostics, and Packaging & Delivery Systems.

Responsibilities :

• Analize different pharmaceutical / life sciences data sets like claims data, electronic medical records, anonymous patient level data, prescription data & sales data, promotional data. Communicate results to clients and onshore team members
• Develop client relationships and serve as an offshore key point of contact for the project
• Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress
• Analyzing the AI / ML algorithms that could be used to solve a given problem and ranking them by their success probability
• Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world
• Verifying data quality, and / or ensuring it via data cleaning
• Supervising the data acquisition process if more data is needed
• Defining validation strategies
• Defining the pre-processing or feature engineering to be done on a given dataset
• Defining data augmentation pipelines
• Training models and tuning their hyperparameters
• Analyzing the errors of the model and designing strategies to overcome them
• Knowledge on Deep learning techniques is added advantage
• Deploying models to production
• Interpreting and analyzing both qualitative and quantitative data points;

hypothesis building with Strong excel and PPT skills

Qualifications :