Sr. Data Scientist

Corning•City of Corning, NC

31d•Onsite

About The Position

The Senior Data Scientist is a highly skilled, experienced analytical professional responsible for leading complex data science initiatives that drive strategic business decisions and operational improvements. Working within the Optical Communications Data and AI organization, this individual applies advanced statistical modeling, machine learning, experimental design, and data visualization to solve high-value problems across manufacturing, supply chain, commercial, and technology functions. The Senior Data Scientist operates with a high degree of independence, mentors junior team members, and serves as a trusted analytical partner to business stakeholders. A Senior Data Scientist in this role will be considered successful when they have demonstrated the following: Business Impact & Value Delivery: Within the first year, successfully contributes to at least two end-to-end data science projects that deliver measurable, documented business outcomes - such as cost reduction, forecast accuracy improvement, or operational efficiency gains - working collaboratively with senior team members and business stakeholders to validate and communicate results. Model Quality & Growing Technical Independence: Consistently delivers well-documented, reproducible, and validated analytical models that meet defined performance benchmarks, demonstrating increasing independence in model selection, feature engineering, and validation methodology—while actively incorporating feedback from senior data scientists and machine learning engineers to improve solution quality and production readiness. Stakeholder Engagement & Communication Growth: Demonstrates steady growth in cross-functional collaboration and communication skills, proactively engaging with business partners to understand requirements, presenting analytical findings clearly and confidently to technical and non-technical audiences, and earning recognition as a reliable and developing analytical contributor within assigned business functions.

Requirements

Bachelor’s or Master's degree in Data Science, Statistics, Applied Mathematics, Computer Science, Engineering, or a related quantitative discipline; PhD a plus.
3+ years of progressive experience applying data science techniques to real-world business problems in an industrial, manufacturing, or technology environment.
Advanced proficiency in Python and/or R, including experience with key libraries (pandas, scikit-learn, NumPy, TensorFlow, PyTorch, etc.).
Deep expertise in statistical modeling, predictive analytics, supervised and unsupervised machine learning, and experimental design.
Strong experience with data visualization tools (e.g., Matplotlib, Seaborn, Tableau, Power BI) and the ability to communicate complex findings visually.
Proven track record of independently managing and delivering complex analytical projects with measurable business outcomes.
Experience working with large, complex datasets from diverse sources (e.g., ERP, MES, IoT, CRM, etc.).
Familiarity with cloud platforms (AWS, Azure, GCP) and big data technologies is a plus.
Strong communication, stakeholder management, and presentation skills.

Nice To Haves

Experience with Databricks, including working within the Databricks Lakehouse Platform for collaborative data science, large-scale data processing, model training, and MLflow-integrated model lifecycle management.
Familiarity with Apache Spark and distributed computing concepts within the Databricks environment.
Experience using Databricks notebooks for exploratory analysis, feature engineering, and model development in a collaborative team setting.
Highly analytical, intellectually curious, and motivated by solving complex, ambiguous problems.
Confident self-starter who takes ownership and delivers results with minimal supervision.
Collaborative mentor and team contributor who elevates those around them.
Strong business acumen and ability to connect analytical work to tangible organizational value.
Adaptable and eager to continuously learn in a fast-paced, innovative environment.

Responsibilities

Design and execute sophisticated analytical solutions, including predictive modeling, statistical analysis, machine learning, and hypothesis-driven research, to address complex business challenges.
Apply domain expertise to frame, scope, and deliver high-impact data science projects with minimal supervision.
Lead exploratory data analysis, feature engineering, and model selection for a wide range of business use cases including demand forecasting, quality improvement, pricing, and operational efficiency.
Collaborate closely with business leaders and cross-functional stakeholders to identify opportunities, define analytical requirements, and translate data insights into clear, actionable recommendations.
Present findings and recommendations confidently to executive and non-technical audiences, emphasizing business impact and decision relevance.
Build strong, trust-based relationships with technology, commercial, supply chain, and manufacturing partners.
Provide technical mentorship and guidance to junior data scientists and analysts, supporting their professional development and analytical skill growth.
Lead and contribute to knowledge-sharing sessions, best practice development, and internal training initiatives.
Participate actively in project reviews, peer feedback, and collaborative problem-solving across the data science team.
Proactively identify and evaluate emerging analytical methods, tools, and technologies relevant to the business.
Champion best practices in reproducibility, documentation, version control, and model governance.
Contribute to the development and refinement of data science standards and workflows across the organization.
Work closely with machine learning engineers to ensure models are properly validated, documented, and transitioned into production environments.
Support model monitoring, performance evaluation, and iterative improvement in deployed solutions.