Audible Content Data Science team partners with business, technology, and product leaders to solve problems related to Content Understanding, Content Evaluation, and Recommendations, relying on Natural Language Processing (NLP), ML, Deep Learning, LLMs/GenAI. We operate in an agile environment in which we own the life cycle of research, design, model development and, often, deployment.

In support of this model, roles have been categorized into one of four work location categories: onsite roles, hybrid roles that are a blend of onsite and remote work, remote local roles that are primarily home-based but require some level of purpose-driven in-person interaction and living within a commutable distance, and remote non-local roles that can be effectively performed remotely with the ability to work in approved states.

To be successful, the ideal candidate will demonstrate a thirst for knowledge and a natural curiosity to enable rapid learning of the Zoetis business model and data & analytics infrastructure at a scale to support a growing business.

Our Hub+Home hybrid workplace model gives employees the flexibility between gathering in a common office space (work from hub) and remote work (work from home). ABOUT YOUYou have a background in developing large scale data systems and an interest in building foundational, reusable, highly-leveraged services and capabilities which other teams or users can depend on.

Enterprise Risk Management Technology within Functions Technology supports global functions partners such as Enterprise Risk Management (ERM), Independent Compliance Risk Management (ICRM), Retail Credit Risk, Operational Risk and Model Risk. Enterprise Risk Management covers Risk Identification, Risk Measurement, Appetite and Limits, Risk Analytics, New Activity Risk Management, Risk Framework and Policy, Enterprise Risk Management Governance.

Machine Learning: The data scientist is familiar with machine learning algorithms and techniques, understanding supervised and unsupervised learning, feature engineering, model evaluation, and optimization.

Experience in working with data science engineers in extraction of the data required for the modeling, understanding the data, handling data quality issues and model monitoring.

Data analytics project experience related to one or more of the following: transaction monitoring data analysis and system implementation, development of customer risk rating models, customer segmentation, threshold tuning, customer/account/transaction data modelling, management and quality assessment, model validation, and/or Know-Your-Customer data remediation.

Core focus will be delivering a sustainable governance model, supported by this role and the Data security team, that evolves in maturity with orchestration and automation. You may possess a vast background (additional certificates help support your deep security knowledge (CISSP, GSEC, CISA, CISM, CRISC, MCSD), comprising of functional experience working in a global hybrid cloud environment, including AWS, Azure, O365, and GCP. Experience with Data Governance or Data Compliance Standards is valuable.

Review authorization limits, approvers & perform Segregation of Duties (SOD) analysis for all MMF systems (Model N, Breakaway and others) Develop knowledge of Model N and other MMF applications at a high-level to assess controls and business risk and monitor system updates.

The successful candidate will work primarily with Assistant Professor of NJIT and be expected to perform the duties of a Data Scholar, developing a deep learning model (generative model) for nonlinear force free extrapolation.

You will implement process to continuously detect model and data drift and remedy to ensure high quality. You will interact with other data scientists, developers, data engineers, UX designers, product managers, and senior executives to understand problem domain, project requirements, users experience and demonstrate model performance and its business value.

Experience applying data preparation methods, feature creation engineering, exploratory data analysis, model creation, and data visualization applied to advanced analytics, machine learning and artificial intelligence.

Developing the MDM Implementation playbook, delivery maps, and estimation model for the Oracle Data & Analytics practice; We help implement and effectively use broad technology offerings including Oracle Analytics and Data solutions along with enhanced Data Governance and Data Management capabilities to solve their business problems and achieve their strategic business goals.

Experience in working with MLOps team to productionalize the algorithms and test the model output. Master’s or Doctorate degree in data science/machine learning/statistics/computer science or equivalent with a background in statistics.