Operations Research Scientist (M/F)
Amazon Luxembourg
Luxembourg City
il y a 5j
source : Monster

Job based in Luxembourg Have you ever ordered a product on Amazon websites and when the box arrived wondered how you got it so fast, how much it would have cost Amazon, and what kinds of systems & processes must be running behind the scenes to power the whole operation?

If so, the European Supply Chain Analytics team is for you. Amazon seeks a passionate, results-oriented, analyst to join our European Supply Chain team in Luxembourg.

You will be responsible to monitor and optimize our complex and expanding fulfilment network end-to-end. Successful candidates will have the attitude of a data detective combined with strong analytic skills and problem solving mindset.

You will work both bottom up from anecdotes up to algorithmic improvements or top down from performance metrics monitoring down to processes improvements.

This highly visible role requires collaborating with software developers and product managers, retail, fulfillment centers, and transportation teams.

You love solving complex problems but you are equally strong at simplifying them for a less technical audience. Your responsibilities include : # Identify and act on opportunities to optimize customer shipments or inventory transfers # Lead complex analysis, develop models and reports to drive key strategic decisions.

Deliver step-changes in supply chain performance through optimization of configurations and parameters, or through improved algorithms and processes.

Collaborate with operations, retail and software teams to implement key strategic initiatives. # Analyze financial impacts and prioritize new features based on their relevance.

Research, evaluate and roll-out software, tools or process improvements. # Track the realized savings and impacts, and communicate results to senior leaders.

BASIC QUALIFICATIONS # Bachelor degree in engineering, operations research, mathematics, statistics, or other quantitative areas such as computer science or physics.

Strong Operations Research background, with hands-on experience in building supply chain / logistics systems prototypes.

Ability to perform quantitative, economic, and numerical analysis of the performance of these systems under uncertainty.

Experience with simulations to support operational decision-making. # Mastery of optimization techniques, including linear and non-linear programming, combinatorial optimization, integer and dynamic programming, network flows.

Experience with Matlab, Python and R and solvers (GUROBI, CPLEX,...) # Knowledge of SQL. Mathematical, analytical, and data-driven decision making skills.

Excellent Microsoft Office skills, including strong working knowledge of Excel and VBA. # Excellent written and verbal communication skills.

Ability to simplify complex topics for broad audiences, as the role requires effective communication with colleagues from computer science, operations research and business backgrounds.

Team player with proven ability to work with cross-functional teams in various locations. # A track record of problem solving and creativity in finding / designing new solutions and innovative methods, using software systems and the desire to create and build new processes.

Ability to handle multiple competing priorities and projects in a fast-paced environment. PREFERRED QUALIFICATIONS # Master’s degree in engineering, operations research, mathematics, statistics, or other quantitative areas such as computer science or physics.

Experience with scripting languages (Python, Perl, or Ruby) and UNIX. # Experience with analyzing big data sets (tables with 100M+ rows and thousands of columns are standard) # Experience working effectively with software engineering teams.

Technical aptitude and familiarity with the design and use of software systems. # Familiarity with supply chain management concepts - forecasting, planning, optimization, logistics - gained through work experience or graduate level education.

Familiarity with statistical concepts and advanced statistical techniques - distributions, confidence intervals, time series analysis, regression models, clustering, machine learning # Familiarity with mathematical modelling & simulation techniques probability, optimization (linear programming, etc.

graph theory, state models # Familiarity with Big Data concepts and platforms (Hadoop, Mahout, etc.)

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