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Senior Applied Scientist

4 months ago

Hoboken, United States Newell Brands Full time
Senior Applied Scientist

Job Number:

2402149United States-New Jersey-Hoboken

The Senior Applied Scientist will play an integral role in supporting a core capability for the company by leveraging large volumes of data, scientific methods, and analytics to drive performance improvement. The role will be highly visible with impact globally across all Newell business units. This role is central to the goal of rapidly increasing our capability to quickly access and analyze data for insights that enable the teams to provide impactful improvements in business critical areas like forecast accuracy, revenue management, consumer journey, media measurement, etc. that will drive the macro company objectives enabling growth, cash conversion and productivity.

Specific Responsibilities

Collaborate with business partners to identify areas where advanced data science techniques can have an impact and deliver insights while improving the organization’s capabilities Apply advanced analytics knowledge to existing business problems to grow revenue or reduce cost Apply analytical rigor and statistical methods to analyze large amounts of data across all channels Apply new technologies and methods in data science, data engineering, and data visualization to improve the technical capabilities of the team Develop and implement diagnostic tools for use by business users to identify performance opportunities and root cause for action planning Create and maintain standards for writing clean, organized code to streamline workflow Create and maintain documentation for learning and knowledge transfer Formulate hypotheses and use existing data to test Find or construct proxies and other means of evaluating the business when ideal data is not available Provide critical thought leadership to enhance organizational capabilities through the application of data analytics Partner with business stakeholders to identify and execute on opportunities for extension and enhancement of capabilities

The Candidate

The successful candidate will have a passion for building infrastructure and enabling data-informed business decisions through sophisticated use of modeling and analytics. They will define measurements, conduct statistical analysis, build models, and surface insights to scale and optimize our business and customer experience. The perfect candidate will have a background in applied statistics, economics or a related quantitative field, will have strong business acumen, experience working with large data sets, and experience interacting with business partners. A self-starter, eager to learn, focused on impact and results with a bias for action, and have demonstrated success in using data science to enhance understanding, progression, and decision making.

  Key Qualifications

5+ years experience in an analytics field, preferably with eCommerce and consumer goods experience. Academic training in a quantitative field such as Computer Science, Mathematics, Statistics, Bioscience, or Engineering. Master’s degree or equivalent professional experience required, PhD preferred. Strong technical background in statistics, measurement, experimentation, and modeling. Extensive experience customizing modern ML systems such as NLP, reinforcement learning, and neural networks. Experience with Bayesian modeling is highly desirable. Experience and functional familiarity with software development and data engineering best practices, code testing, and using CI/CD tools in the workflow. Experience designing custom systems using frameworks such as PyTorch and PyMC. Extensive experience using the Python data science/ML stack in a collaborative development environment using version control tools (git). Good command of SQL. Experience with cloud computing platforms such as AWS, Azure, and GCP and containerized solutions such as Docker. Experience with Kubernetes or scaled deployments using ray a plus. Expert in mathematical models underlying data science methods, understanding the trade-offs between different solutions. Expert level statistical forecasting skills in a business environment. Strong written and oral communication skills and the ability to use these to convey results comprehensibly to a non-technical audience. General interpersonal savvy. Ability to work with and connect with all levels of employees. Experience working in a matrix environment a plus. Strong planning and organizational skills. Initiative and follow-up, including a strong sense of autonomy and bias for action. Strong character – ability to champion the right decision in difficult situations. Demonstrated ability to handle multiple tasks and assignments simultaneously.