Optimizing ShipBob's end-to-end supply chain (inventory placement, fulfillment, transportation) for cost and quality, and supporting a complex network through advanced analytical methods, modeling, and simulation.
Requirements
- 3+ years of building models for business application experience.
- Experience programming in Java, C++, Python or related language.
- Hands-on experience with optimization solvers like Gurobi or CPLEX, and modeling frameworks like Pyomo or OR-Tools is required.
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing, neural deep learning methods, machine learning.
- Experience in applied machine learning and cloud deployment skills (particularly Azure ecosystem) preferred.
Responsibilities
- Work across fulfillment, transportation, and revenue teams to optimize our fulfillment center topology to reduce cost and improve service.
- Collaborate with product, engineering, and data science teams to develop algorithms, improve first time product placement, and develop transportation cost savings entitlements for thousands of merchants.
- Run and execute machine learning and modelling projects/products end-to-end: from ideation, analysis, prototyping, development, metrics, through to production and monitoring.
- Identify opportunities to reduce costs and transit times through new middle mile connections and improvements to parcel carrier allocations.
- Brief complex studies and analyses related to network optimization to senior level leadership (Director to C-Suite).
Other
- Grow with an Ownership Mindset
- Collaborate with Peers and Leaders Alike
- Experience a High-Performance Culture and Clear Purpose
- Self-motivation, analytical thinking, a data-focused approach, and effective collaboration with various stakeholders across operations, supply chain, finance, revenue, product, and engineering.
- Excellent verbal and written communication skills.
