Data Scientist, Content Strategy, Remote, NJ, USA, Global Leading Organization


Data Scientist, Content Strategy

Role Overview

We are hiring for an ambitious Data Scientist to work with an inspirational company who leads the way with innovating and inspiring audiences through the power of voice. Their perspectives and experiences power their ideas and come together in their mission to unleash the power of the spoken word.

As a Data Scientist, this is an exciting opportunity to apply your experience and passion for modeling and Machine Learning (ML) to figure out which piece of content is going to be successful next. You will be improving and creating new capabilities for content discovery and recommendations and eager to innovate and build scalable solutions.

The Content Data Science team partners with business, technology, and product leaders to solve problems related to content evaluation and recommendations, relying on cutting-edge ML, deep learning techniques, and Natural Language Processing (NLP). They operate in an agile environment in which they own the life cycle of research, design, model development, and, often, deployment.

Data Scientists are members of an interdisciplinary research team with an integral role in the design and integration of models to support or automate decision-making throughout the business in every country. Team members should be capable of modeling in the many areas of the business-related, but not limited to content.

Data Scientists must be able to discuss and present their research in any level of detail with their peers and business stakeholders. They are expected to acquire support and partnership from personalities through the company, including engineering and research teams within the global business. Successful data scientists are expected to influence and mentor each other, influence and mentor their peers globally, and influence and mentor their business partners.

Key Responsibilities

  • Identifying necessary, relevant, and novel data sources residing throughout the company, from linked business, from third-party vendors, from government agencies, and from novel – yet to be acknowledged sources
  • Acquiring data, which often means building the necessary SQL / ETL queries, import processes through various company-specific interfaces
  • Exploring data will occupy the largest portion of attention, and should be second nature in order to deeply understand the phenomenon being modeled, and the validity and reliability of the inputs
  • Analyzing and decomposing the written/spoken word, the story, the author: conduct research on problems specific to the Natural Language Processing field, develop prediction models that can go directly into production, and build working prototypes
  • Fluency in Python • Validating models against alternative approaches, expected and observed outcome, and numerous directly and indirectly relevant business defined key performance indicators
  • Reviewing models of peers for the purpose of reducing and managing risk to the business, and maximizing improvement of business practice and customer experience
  • Implementing models from the initial evaluation of the computational demands, accuracy, and reliability of the relevant ETL processes, and the integrity of the data sources in production, to the computational demands, accuracy, and reliability of the model training and/or scoring processes in the production environment • Model management will include developing sustainable, consumable, accurate, and impactful reporting on model inputs, model outputs, observed outputs, business impact, and key performance indicators
  • Link content attribute to various consumption metrics through the use of Machine Learning / predictive modeling • Build models for catalog optimization in terms of audience satisfaction
  • Build the pipelines and processes to provide the business stakeholders timely, relevant insights, scenarios and recommendations
  • Build new content-based personalized recommendation strategies
  • Thrive in complex business environments defined by uncertain, incomplete or limited information.

Experience & Qualifications

  • Master’s degree in Data Science, Machine Learning, Statistics, Computer Science, Applied Math, Operations Research, Economics, or a related field
  • 1+ year experience in industry (can be combined experience as intern/contractor)
  • 2+ years in building statistical models and machine learning models using large datasets from multiple resources • 3+ years working with Python
  • 1+ year with non-linear models such as Neural Nets / Deep Learning and Gradient Boosting
  • 1+ year working with Customer, Content, or Product data modeling and extraction
  • 1+ year of working on problems specific to the Natural Language Processing field
  • 1+ year using database technologies such as SQL / ETL
  • Ability to translate complex models and analysis results into layman terms Additional beneficial qualifications
  • Ph.D. degree in the disciplines mentioned above
  • 4+ years in building statistical models and machine learning models using large datasets from multiple resources • 3+ year with non-linear models such as Neural Nets / Deep Learning and Gradient Boosting
  • Extensive experience applying theoretical models in an applied environment
  • Familiarity with AWS
  • Verbal and written communication and data presentation skills – with experience presenting in scientific conferences

This is a company who is committed to a diverse and inclusive workplace. An equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.