Követelmények

  • Good communication skills

             
  • Be hungry to tackle challenges

             
  • Passionate about user behavior

             
  • Experienced in quantitative analyses

             
  • R or Python experience

             
Budapest, Szép u. 4, 1053 Magyarország

 

Munka leírása

As a Data Scientist, you will be analyzing large data sets of behavioral data (web analytics, ecommerce transactions) and product attribute data and developing predictive models that provide insight into customer preferences. These predictive models form the backbone of our services.

Feladatok

  • Analysis of data sets about user behavior
  • Developing algorithmic approaches
  • Using different data science modeling techniques
  • Collaboration with our R&D and developer teams
  • Work with our Analytics/BI group

Állás részletei

Szerződés típusa
Open ended
Fizetés típusa
Fixed monthly salary
Bruttó fizetéssáv
Nincs megadva
Bónusz
Stock options

Munkaidő

Weekly working hours
4 hours
Flexible working hours  - Upon discussion
Core time
10:00 - 16:00
Home office  - Upon discussion

More on the job

Our products, powered by data science, are integrated into the largest fashion and apparel ecommerce stores in the US and Europe and provide shopping experiences that revolutionize online commerce.

Your work will directly impact our 50+ million users and ensure their happiness. If you are passionate about working with user behavior data, big and small, this is your opportunity.

Responsibilities #2

  • Analysis of our data sets containing detailed information about how people interact with our partners’ websites and our services
  • Finding data points that are predictive for various types of behavior using modeling techniques ranging from machine learning, AI to statistical models
  • Developing, testing and maintaining data science driven, algorithmic approaches
  • Support algorithm fine-tuning and service integration
  • Find useful, actionable information in large data sets containing web analytics data

 

The right tool for the job - Tamás

Technology Stack

We believe in using the right tool for the right job instead of a particular set of technologies. Experience with our current stack is useful, but not required.

  • Frontend: ES6, AngularJS, React/Flux
  • Backend: Java 8 (gRPC, Spark), Go, MongoDB, PostgreSQL
  • Admin: Ruby/Rails (sidekiq), PostgreSQL
  • Data: Python (Airflow, Spark), EMR, Redshift, S3
  • DevOps: AWS, GitHub, Circle CI, HipChat, Sentry, New Relic, Docker, Packer, Terraform, Ansible
  • QA: BrowserStack, JIRA, Java (Selenium)

 

Collaborative culture - Kyra

Relocation

We love people who want to move to Europe or want to move to Budapest within Europe. Our open arms policy includes relocation benefits so we pay for your move.

Don't hesitate too long. Go, try and learn.

Emese Pogácsás VP Engineering

Mit csinálunk

We are a San Francisco based tech startup in the fashion/apparel retail space building innovative shopping experiences based on retailers' data. Our customer base includes some of the most iconic brands in the US such as Saks Fifth Avenue, Barneys New York, Bloomingdale's, Lands' End and others. Our services are branded experiences inside these stores and are used by over 40 million users a month.

You can touch the business - no matter which team you're at.

Julianna Göbölös-Szabó Data Scientist

I always know why I do what I am doing and how the results will influence the company.

Ágnes Salánki Data Analyst

 

Mentoring, mentoring, mentoring - Ágnes

Benefits

  • Be part of a successful startup with phenomenal growth opportunities
  • Casual, fun, international team
  • Competitive compensation package, flexible vacation days
  • Stock options
  • Cool downtown office with games, beer, wine and more

A munkahelyről

1

Is there lunch break?
Of course! And often we do it together.

2

Men/Women ratio
60% / 40%

3

How do we give feedback?
We have two-way sessions every 3 months

Cutting edge technology used to solve real, hard problems.

Gábor Rátky CTO

Értékeink

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1

Team orientation

2

Dependability

3

Customer care


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