Mid Level Data Scientist

In this vital role, you should possess robust knowledge and hands-on experience in machine learning. Subareas of particular importance to the team include natural language processing, image processing, audio processing, and generative AI. Additional capabilities in mechanism-based mathematical modeling, advanced statistical modeling, or causal modeling would receive special consideration. Furthermore, domain knowledge in one of biopharmaceutical R&D disciplines is strongly desired.Core Responsibilities for the Role:Work collaboratively across disciplines with an overt sense of ownership.Apply programming to parse data, statistics, machine learning, and artificial intelligence techniques, and NLP algorithms to uncover novel trends or factors representing opportunities or challenges to our business.Implement data science projects and complete high-quality deliverables on time to support business.Transform business, medical, or scientific questions into analytical ones and map out solutions, delivery, and impact.Communicate effectively and influence a diverse set of technical, scientific, medical, and business constituents at the functional and executive levels.Employ unsupervised and supervised techniques to develop predictive or prescriptive models with reliable performance, interpretability, and actionability.Develop generative models for a variety of applications. Contribute to the ongoing development of the team by providing ideas and implementing standard methodologies. Stay updated on industry and academia advancements, assimilating the latest AI/ML paradigms and tools to enhance our capabilities.What we expect of you:The professional we seek should have these qualifications.Basic Qualifications:Master’s degree ORBachelor’s degree and 2 years of experience in a quantitative field ORAssociate degree and 4 years of experience in a quantitative field ORHigh school diploma/GED and 6 years of experience in a quantitative field.Preferred Qualifications:Experience in designing and building a variety of supervised and unsupervised machine learning models.Experience in causal modeling and demonstrated evidence of the use thereof to inform decision making in scientific, medical, or business settings.Proficiency in the use of one (or more) of these methodologies with demonstrated and interpretable insight and impact: generative artificial intelligence (Gen AI), deep learning, natural language processing, large language models.Experience with Healthcare data, e.g., clinical trial data, electronic medical records, and insurance claims; or Bioscience’s data, e.g., protein or small molecule data, or bioinformatics; or Biopharmaceutical manufacturing.Strong proficiency in Python and experience in SQL.Experience with cloud computing technologies, e.g., AWS, Spark.Experience with source code control technologies, e.g., Git.Experience delivering models for production use cases.

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