Job Location: Raleigh, NC
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- Analyze complex business problems using data from multiple internal and external sources to provide insights to decision-makers and business stakeholders across domains such as Finance, HR, Corporate Operations, Corporate Development, Compliance, Sales, Marketing, R&D and Pharma Drug Manufacturing (PDM)
- Work cross-departmentally to identify and prioritize Data Science initiatives such as Predictive, Prescriptive, Diagnostic Analytics, Forecasting, Anomaly detection, Preventive Maintenance, Edge Inference and RPA/IPAs
- Develop, implement and optimize Data Science solutions on AI/ML platform built on AWS, using Machine Learning, Deep Learning and NLP frameworks, libraries and platforms
- Build complex Data Prep and Data Engineering workflows to come up with high quality data sets for AI/ML use cases
- Develop curated Feature Sets using Feature Engineering to support specific AI/ML use cases
- Train and Build AI/ML Models
- Manage and Optimize Model Performance to achieve desired accuracy and to minimize errors
- Build dashboards and visualizations to understand Model predictions
- Work collaboratively with cross-functional stakeholders to ensure sign-offs of the AI/ML models
- Lead or manage vendors by providing guidance, take ownership and responsibility for high-quality results
- Master’s Degree with Data Science, Math or Statistics major with 8 years of experience
- PhD in Statistics or Data Science with 4 years of experience
- Extensive experience with all aspects of data management including data modeling, data mining, data warehouses, reporting & data visualization, including experience in Spark for Data Engineering and familiarity with Parquet data formats
- Proficiency with AWS Cloud architecture and Services such as S3, Lake Formation, Glue, RedShift, EMR, IAM, Cloud Formation
- Proficiency in AWS AI/ML Services such as SageMaker, SageMaker Neo, Augmented AI, Amazon Forecasting, Deep Learning AMIs, Gluon, Greengrass, etc; Analytics Services such as Kinesis, IoT etc; and Statistical techniques and Tools such as SAS etc.
- Proficiency in ML & Deep Learning frameworks such as PyTorch, TensorFlow, Keras etc.
- Excellent understanding and usage of Supervised, Unsupervised and Reinforcement learning techniques, Dimensionality Reduction techniques, and algorithms such as Linear regression, Logistic regression, Linear/Quadratic discriminant analysis, PCA, SVM, K-Means, Hierarchical clustering, K-Nearest Neighbors, Decision Trees, Gradient-boosting trees, Random Forests, Naive Bayes; Neural networks such as CNNs, RNN, LSTMs; and Recommender systems
- Life Science experience is a plus
- Strong Business Partnering and presentation skills
- Work independently with ‘can-do’ attitude and willingness to learn and adopt evolving technology
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Integrity (Doing What’s Right)
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Inclusion (Encouraging Diversity)
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Teamwork (Working Together)
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Excellence (Being Your Best)
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Accountability (Taking Personal Responsibility)
If this is not the right move for you now but remain interested in a career at Gilead Sciences, please connect with us via our Career Site:
for assistance.
Following extensive monitoring, research, consideration of business implications and advice from internal and external experts, Gilead has made the decision to require all U.S., Canada, Australia, Singapore, and Hong Kong employees and contractors to receive the COVID-19 vaccines as a condition of employment. “Full vaccination” is defined as two weeks after both doses of a two-dose vaccine or two weeks since a single-dose vaccine has been administered. Anyone unable to be vaccinated, either because of a sincerely held religious belief or a medical condition or disability that prevents them from being vaccinated, can request a reasonable accommodation.
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