South San Francisco, California, United States of America
Type
Full Time
Responsibilities:
You will lead and guide data science and data engineering elements of AI/ML projects with an enterprise approach.
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 partners define clear, impactful business priorities, and use your 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 domain expert for applicable data science and other advanced analytical methodologies, programs, and projects in relation to recommendation engines.
Collaborate within multi-functional teams to develop efficient recommendation systems, gain alignment and deliver impactful business insights; engage necessary partners to enable better decision-making.
Openly share perspectives and insights to elevate team thinking and drive a balanced, integrated point of view; effectively weigh and communicate trade-off considerations.
This role is based in South San Francisco and relocation benefits are not available.
Requirements & Skills:
You have a BS, MS, or PhD in Data Science; Computer Science; Applied Mathematics, Statistics, Economics, Engineering or related field with a focus on Artificial Intelligence (AI) or Machine Learning (ML)
3-6 years of data science, artificial intelligence and/or machine learning work experience (transferable skills across multiple industries is a huge plus)
Experience in the following quantitative fields: Deep learning, Reinforcement Learning, Causal Inference, A/B Testing, Multi-armed Bandits, Supervised and Unsupervised Machine Learning, and Design of Experiments
Demonstrated proficiency in leveraging open source libraries and packages such as Spark, TensorFlow, Keras, PyTorch to solve Enterprise problems
Proficiency in modeling techniques such as content based filtering, collaborative filtering, clustering etc.
Solid foundational knowledge in Deep Learning (open source frameworks like Tensorflow, Keras and/or PyTorch) with applications focused on Recommendation Engines (such as feed forward neural networks, convolutional neural networks, or recurrent neural networks)
Experience working with large, complex data using Hadoop or Spark or any other big data platforms
Proficiency with industry-standard programming languages such as R or Python
Experience with other data science and cloud-computing tools and platforms (AWS, GCP, etc.)
Proficiency using ML in a variety of contexts, particularly text classification and clustering across CMG use cases (medical, commercial and digital)
3+ years of experience applying Recommender Systems to enterprise data, and demonstrated ability to drive tangible results
Experience acting as a strategic thought partner to teams; demonstrated ability to solve problems and think creatively.
Strong knowledge of secondary data sources including syndicated sales, promotional & marketing data, longitudinal patient level data; experience with payer data is preferred
Proven track record of leadership, time-management, project management, and teamwork; Strong attention to detail
Experience translating research or analysis to communicate (in presentations and in writing) concise and compelling business stories that influence decisions and strategy
