Quantitative Analyst

Vertragsart:
Vor Ort
Start:
keine Angabe
Dauer:
keine Angabe
Von:
Harvey Nash IT Recruitment Switzerland
Ort:
Zürich
Eingestellt:
07.12.2015
Land:
flag_no Schweiz
Projekt-ID:
1032380

Warning
Dieses Projekt ist archiviert und leider nicht (mehr) aktiv.
Sie finden vakante Projekte hier in unserer Projektbörse.

For a 12 month project with our IT client in Zurich, we are looking ASAP for a Quantitative Analyst

The Shopping Engineering team is looking for a motivated quantitative analyst to help shape the face of how the company's Shopping performs data experiments. Experimenting with the large amount of product data in Shopping presents unique challenges both in terms of scale and analysis.

As Quantitative Analyst for Shopping Data Experiments you would be in the unique position to work with multiple software engineering teams in Shopping to guide their experimental designs and evaluate how their experiments affect Shopping user experience and revenue.

At our client, data drives all of our decision-making. Quantitative Analysts work all across the organization to help shape the company's business and technical strategies by processing, analysing and interpreting huge data sets. Using analytical rigor and statistical methods, you mine through data to identify opportunities for the company and their clientss to operate more efficiently, from enhancing advertising efficacy to network infrastructure optimization to studying user behaviour. As an analyst, you do more than just crunch the numbers. You work with Engineers, Product Managers, Sales Associates and Marketing teams to adjust the comapanys practices according to your findings. Identifying the problem is only half the job; you also figure out the solution.

Minimum Qualifications:
- MS in Statistics or Econometrics or a related field, or equivalent practical experience in the analysis and modelling of large, complex, noisy data.
- Experience with R or MATLAB.

Preferred Qualifications:
- Some exposure to SQL.
- Familiarity with experimental design principles.
- Ability to draw conclusions from data and recommend actions.
- Demonstrated willingness to both teach others and learn new techniques.

Responsibilities:
- Apply advanced statistical methods and work with large, complex data sets.
- Solve difficult, non-routine problems, and clearly communicate results and methods
- Interact cross-functionally with a wide variety of people and teams.
- Lead investigations into multiple streams of Shopping serving data.