Data Scientist – OMP Supply Chain Planning (Remote)
Cognizant · San Francisco District
Description du poste
About the role
This hybrid position seeks an experienced data scientist with deep expertise in OMP Sales and Operations Planning and OMP Supply Chain Planning to develop advanced analytics and optimization solutions for global supply chain and manufacturing operations.
Key responsibilities
- Design and implement predictive and prescriptive models to improve OMP S&OP accuracy and support demand‑supply balancing.
- Develop analytical frameworks that integrate OMP processes with enterprise data for scenario analysis and long‑term planning.
- Create optimization algorithms embedded in OMP workflows to reduce inventory costs and increase service levels.
- Build scalable data pipelines and feature‑engineering logic to ensure high‑quality inputs for planning models.
- Evaluate model performance with statistical techniques and business metrics, providing dashboards and reports for planners.
Required profile
- 8‑12 years of data‑science experience applying analytics and optimization to planning challenges.
- Hands‑on expertise with OMP Sales and Operations Planning and OMP Supply Chain Planning configuration.
- Strong knowledge of manufacturing and supply‑chain processes, including production, inventory, and distribution.
- Ability to work in a hybrid environment, collaborating with cross‑functional teams and managing independent tasks.
Required skills
- OMP Sales and Operations Planning (S&OP) platform.
- OMP Supply Chain Planning platform.
- Statistical modeling, machine learning, and optimization techniques.
- Programming and data‑manipulation tools for large structured and semi‑structured datasets.
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Cognizant
San Francisco District