Data Scientist - NLP

Data Science And Data Engineering ManagerManage and guide data science and data engineering elements of AI/ML projectsDrive the development, deployment and industrialization of Enterprise NLP engines to generate value from unstructured data for the Commercial and Medical organizations.Develop a deep understanding of customers and market dynamics, and apply your technical skills to advance that knowledge and guide the organization, delivering products that help create an integrated customer experience and drive impact.Help stakeholders define clear, impactful business priorities, and where possible, use their own expertise or existing analysis/research to influence and guide.Apply ML and applied statistics approaches to answer a variety of complex business questions using multiple data sources and technical tools.Act as subject matter expert for applicable data science and other advanced analytical methodologies, programs, and projects.Collaborate within cross-functional teams to develop solutions, gain alignment and deliver impactful business insights; engage necessary stakeholders to enable better decision-makingOpenly share perspective and insights to elevate team thinking and drive a balanced, holistic point of view; effectively weigh and communicate trade-off considerationsTake an enterprise mindset, linking individual responsibilities with broader organization; focus on outcomes that provide most business valueDemonstrate self accountabilityLook for opportunities for continuous improvement; engage managers and peer group regularly for coaching, assistance, and advocacyAct as a thought partner and advisor to all relevant teams and stakeholders; look for and establish opportunities for peer mentorshipQualifications:MS or PhD in relevant discipline: Data Science; Computer Science; Applied Mathematics, Statistics, Economics, Engineering or related field, with a focus on Artificial Intelligence (AI) or Machine Learning (ML) preferred10+ years of relevant industry work experience (transferable skill set across multiple industries is a huge plus)Significant (5+ years) experience in the application of Natural Language Processing (NLP) and Text Analytics to Enterprise data, and demonstrated ability to drive tangible resultsSignificant experience in most of the following quantitative fields: Natural Language Processing, OCR, Computer Vision, Information Retrieval, Machine Comprehension, Question Answering, Reinforcement Learning, Knowledge Graph, Causal Inference, and Design of ExperimentsDemonstrated proficiency in leveraging open source NLP libraries and packages such as TextBlob, spaCy, NLTK, HuggingFace etc. to solve Enterprise problemsDeep proficiency in Text Processing and modeling techniques such as Latent Semantic Analysis, Keyword, Entity and Relationship Extraction, Topic Modeling, Document Parsing and Tagging, Document Clustering etc.Solid foundational knowledge in Deep Learning, with applications focused on NLP (such as Transformers for Sequence to Sequence learning tasks)Experience building Deep Learning based models using popular open source frameworks like Tensorflow, Keras and/or PyTorchExperience working with large, complex data using Hadoop or Hive or any other big data platformsProficiency with industry-standard programming languages such as R or PythonExperience with other data science and cloud-computing tools and platforms (AWS, GCP, etc.)Strong knowledge of secondary data sources including syndicated sales, promotional & marketing data, longitudinal patient level data; experience with payer data is preferredProficiency using ML in a variety of contexts, particularly text classification and clustering across CMG use cases (medical, commercial and digital)Experience with data visualization tools such as tableau, and/or qlik, and/or data studio etcExperience acting as a strategic thought partner to teams; demonstrated ability to solve problems and think outside the boxProven track record of leadership, time-management, project management, and teamwork; Strong attention to detailExperience translating research or analysis to communicate (in presentations and in writing) concise and compelling business stories that influence decisions and strategy