Act as the principal data scientist for the D&IT AI Platform & Products organization, providing subject matter expertise in the space of data science representing D&IT and partnering with the Corning businesses’ data science organizations.
Translate ambiguous business problems into data science problems, analyzing complex datasets to inform or make decisions to address these problems.
Apply scientific principles and concepts to support significant invention, using a range of scientific methodologies and following best practices for those methodologies.
Proactively identify opportunities and solutions to problems, even if these solutions require novel techniques or approaches.
Create metrics and measurements to quantify the business impact of your work.
Write accurate and clear technical documentation, whitepapers, and reports, incorporating the appropriate level of mathematical rigor as needed.
Communicate the approach and outcomes of models clearly and effectively with non-technical stakeholders to build trust.
Collaborate with stakeholders across a broad range of functions, such as science, data, engineering, product, and business.
Participate in business center of excellence projects, providing data analytics, machine learning, software development, and modeling support to the Corning technology community to enable effective innovation and competitive advantage of Corning products.
Interface with customers to define project specifications, research and improve applicable technologies, and work with industry experts to expand Corning’s expertise in key emerging technology areas.
Work independently or as part of a team on data preparation, machine learning model development and testing, and/or software development & modifications in support of Corning products at different stages.
Interpret data analysis results in a business context and articulate the implications of the results to the business function.
Write and maintain relevant support documentation, prepare and deliver relevant user training, and mentor new engineers and scientists.
Influences multiple teams to build consensus and advise senior leadership.
Evaluate cross-team perspectives and understand how interactions among teams, processes, and systems need to be modelled in solutions.
Utilize deep understanding of business challenges and the applicability of relevant data science disciplines and interactions amongst systems to identify the most advantageous solutions that deliver significant benefit to the business
Contributions influence technical and business strategy. Harmonize discordant views and lead the resolution of contentious issues.
Optimize connected systems using their dynamics. Improve consistency and integration between the team’s solutions and the work done by related teams.
Improve work done by others either via collaboration or by increasing their scientific knowledge using specialized tools or advanced techniques
Requirements & Skills:
Min. 8-10 years’ experience in data science or related field.
Capable of independently preparing data for machine learning purposes and performing exploratory data analysis.
Deep knowledge of machine learning techniques, such as traditional machine learning and deep learning algorithms, convolutional neural networks, recurrent neural networks, generative models, and reinforcement learning algorithms.
Background in Time Series, Computer Vision, Natural Language Processing and track record of journal or conference publications in relevant fields or demonstrated practical applications of machine learning algorithms in manufacturing or business environment.
Practical experience of using machine learning and deep learning frameworks and libraries for large-scale data mining (e.g., PyTorch, Tensor Flow)
Strong mathematical and programming skills using Python and/or Databricks.
Comfortable working with Windows and Linux parallel processing clusters.
Strong interpersonal and presentation skills and the ability to work as a team player or individual contributor. Must be a proactive and solution-oriented problem solver.
Knowledge of emerging technologies and algorithms in computer vision and/or machine learning in general (e.g., attention algorithms, few-shot learning, explainability, generative networks).
Ability to support multiple projects at the same time.
Background in one or more scientific disciplines (e.g., physics, chemistry, or engineering).
Demonstrated ability to build data analytics solutions to support the required performance and scale.
A foundation in software design principles and an ability to design and create applications as necessary.
Clear dedication to excellence and advancing beyond the current state.
Strong personal motivation and proven ability to embrace and drive change.
