Offers “Amazon”

Expires soon Amazon

Ops Research Analyst

  • Seattle (King)
  • Logistics

Job description



DESCRIPTION

Amazon Fulfillment Services is looking for a motivated individual with strong analytical skills and practical experience to join our North America Amazon Customer Excellence Team (NA ACES) team. We are hiring specialists at all levels into our team with expertise in machine learning, network and combinatorial optimization, modeling and simulation, multi-criteria decision analysis, algorithm design, and/or control theory.
Amazon is world's most customer centric company with relentless focus on on-time delivery, wide selection and lower costs. SME teams' main focus is in the development of analytical strategic models and automation tools fed by our massive amounts of available data to enable a more efficient supply chain network and lower cost of operations. You will be responsible for building these models/tools that improve the economics of Amazon's NA fulfillment networks. You will identify and evaluate opportunities to reduce variable costs by improving the fulfillment center processes, transportation operations and scheduling, and the execution to operational plans. You will also improve the efficiency of capital investment by helping the fulfillment centers to improve storage utilization and the effective use of automation. Finally, you will help create the metrics to quantify improvements to the fulfillment costs (e.g., transportation and labor costs) resulting from the application of these optimization models and tools.

Optimization algorithms are particularly important is in the design and operation of our fulfillment centers (FC). Each FC holds millions of items that can be ordered in any combination, challenging Amazon to get the items ordered by a customer into a single box and delivered to the customer on time. The pick, pack, and ship process is controlled by software algorithms that automate operations decisions to optimize the use of constrained resources to minimize the operational cost while meeting all deadlines; algorithms also guide the movement of people within the fulfillment center to balance the flow of work across the processes. In addition, optimization and approximation algorithms play a pivotal role in the design of the processes and equipment within the fulfillment centers. Better algorithms will enable us to reduce variable costs and reduce capital investments. In this team, you will work on the aforementioned and analyze process designs, build mathematical models of material flow, scheduling, staffing, and resource planning to analyze throughput and identify constraints, create simulations to improve physical process and support decision making for operations managers by driving improvements in cost, quality, and safety.

What you design and build will then be implemented worldwide!

Some examples of innovations led by our team include:

· Dock to dock flow and Directed staffing- Use heuristic and predictive modeling to determine optimal associate staffing into each work cell, resulting in minimized imbalance and degradation. This reduces the number of labor moves required to manage any direct work function.

. Item dimension Measurement- Leverage big data and anomaly detection methods to surface variance in item dimension data across millions of ASINS with the aim of building capability to enable machine learning models to automate and lock down measurement
. Delivery Estimate Accuracy- To increase supply chain speed and quality, Fulfillment center (FC) processes are broken down in individual buckets (order generation to shipment) to analyze FC performance for service level agreement (SLA) targets for each of them. Critical Amazon Metrics Platform will summarize these buckets and performance tracking will be done on a near real time basis. Similarly, productivity analysis and long-term production planning should be estimated with optimal backlogs without customer expectation misses. These analyses will be used to model FC performance at each process based on a TP10 to TP99 distribution to improve delivery accuracy with increased productivity levels.

Desired profile



BASIC QUALIFICATIONS

· Bachelor's degree required (Master's Degree preferred) in a quantitative discipline (Statistics, Mathematics, Industrial Engineering, Operations Research, Management Science, Economics, etc.)
· 5+ years' experience in Statistics, Mathematics, Industrial Engineering, Operations Research, Management Science, Economics, or related field Or a Master's degree with 3+ years.
· Knowledge of Analytical Problem solving tools including Statistical Hypothesis testing, Optimization heuristics, Simulation, Queueing Analysis
· Proven experience in applying Math/Stat/analytical tools in the CPG/Retail environment for optimization problems
· Experience using either JMP or Minitab, SAS, SPSS, R, etc.
· Ability to travel both domestically and internationally – up to 30%

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