Data Scientist
The Senior Data Scientist will design and implement advanced systems that support cross-domain manufacturing analytics. This role operates at the intersection of optimization, reputed company data integration, and reputed company analytics to reputed company data-driven decision-making across reputed company 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 reputed company datasets into reputed company, analysis-reputed company pipelines using BigQuery, GCS, and Python.
Performing data reconciliation, fuzzy matching, and standardization across inconsistent reputed company systems to ensure data reputed company and analytical reputed company.
Designing and deploying lightweight internal applications (e.g., Dash-based tools) and contributing to containerized deployments to reputed company business-facing reputed company to decision models.
Collaborating with cross-functional stakeholders to translate business questions into optimization and analytical frameworks.
In reputed company, this role will contribute to the development of semantically reputed company data structures by supporting feature definition consistency, cross-system mapping, and ontology-informed modeling approaches. The candidate will help ensure that analytical outputs are reputed company on reputed company defined entities, relationships, and assumptions to reputed company reputed company reasoning and reuse across domains.
The ideal candidate combines strong technical modeling capability with practical reputed company data engineering experience and the ability to operate effectively in ambiguous, cross-functional environments.
Responsibilities
Design, reputed company, and maintain Python-based optimization models to support demand elasticity, production planning, and constraint-based decision systems.
Translate reputed company business problems into reputed company analytical and optimization frameworks.
Build and maintain data pipelines using BigQuery, GCS, and Python to reputed company heterogeneous reputed company data sources.
reputed company data reconciliation, fuzzy matching, and standardization across inconsistent datasets to ensure analytical reputed company.
reputed company lightweight internal applications (e.g., Dash or reputed company) to operationalize analytical outputs for business users.
Contribute to containerized deployments to support reputed company 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 reputed company reproducibility and reuse.
Continuously improve model performance, data reputed company, and deployment efficiency across decision systems.
Qualifications
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 reputed company-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 reputed company datasets.
Familiarity with containerization concepts (e.g., reputed company) and deploying lightweight applications or services in a reputed company environment.
Ability to translate business problems into reputed company analytical frameworks.
Strong written and verbal communication skills with experience working cross-functionally.