The Senior Data Scientist will design and implement advanced systems that support cross-domain manufacturing analytics. This role operates at the intersection of optimization, enterprise data integration, and applied analytics to enable data-driven decision-making across complex business workflows.
Key responsibilities include:
Developing and maintaining Python-based optimization models to support demand elasticity, production planning, and constraint-based decision frameworks.
Integrating heterogeneous enterprise datasets into structured, analysis-ready pipelines using BigQuery, GCS, and Python.
Performing data reconciliation, fuzzy matching, and standardization across inconsistent source systems to ensure data quality and analytical integrity.
Designing and deploying lightweight internal applications (e.g., Dash-based tools) and contributing to containerized deployments to enable business-facing access to decision models.
Collaborating with cross-functional stakeholders to translate business questions into optimization and analytical frameworks.
In addition, this role will contribute to the development of semantically aligned data structures by supporting feature definition consistency, cross-system mapping, and ontology-informed modeling approaches. The candidate will help ensure that analytical outputs are built on clearly defined entities, relationships, and assumptions to enable scalable reasoning and reuse across domains.
The ideal candidate combines strong technical modeling capability with practical enterprise data engineering experience and the ability to operate effectively in ambiguous, cross-functional environments.
Design, develop, and maintain Python-based optimization models to support demand elasticity, production planning, and constraint-based decision systems.
Translate complex business problems into structured analytical and optimization frameworks.
Build and maintain data pipelines using BigQuery, GCS, and Python to integrate heterogeneous enterprise data sources.
Perform data reconciliation, fuzzy matching, and standardization across inconsistent datasets to ensure analytical integrity.
Develop lightweight internal applications (e.g., Dash or streamlit) to operationalize analytical outputs for business users.
Contribute to containerized deployments to support scalable and maintainable delivery of decision tools.
Partner with cross-functional stakeholders to define requirements and validate outputs.
Support semantic alignment across systems by contributing to feature definition consistency, cross-system mapping, and ontology-informed data structures.
Document modeling assumptions, data transformations, and system dependencies to enable reproducibility and reuse.
Continuously improve model performance, data quality, and deployment efficiency across decision systems.
Bachelor’s degree in Data Science, Engineering, Mathematics, Computer Science, Operations Research, or equivalent field.
3+ years of experience developing analytical or optimization models in Python.
Experience building and maintaining data pipelines using SQL and cloud-based data platforms (e.g., BigQuery, GCS).
Strong proficiency in Python for data analysis and modeling (e.g., pandas, NumPy, Pyomo or similar optimization libraries).
Experience integrating and standardizing heterogeneous enterprise datasets.
Familiarity with containerization concepts (e.g., Docker) and deploying lightweight applications or services in a cloud environment.
Ability to translate business problems into structured analytical frameworks.
Strong written and verbal communication skills with experience working cross-functionally.
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