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Applied Scientist - Merchandise Integrity(m/w/x)
Prototyping anomaly detection systems for product data in an exclusive online shopping club. 3+ years industry experience developing anomaly detection and risk scoring required. 30 days work from abroad, employee shares program, 40% fashion discount.
Requirements
- Master's degree or PhD in data science, statistics, mathematics, physics, economics, or related quantitative discipline
- At least 3 years of industry experience in developing anomaly detection and risk scoring
- Extensive experience with Python and data science libraries
- Excellent verbal and written communication skills
- Curious and self-starter mindset
- Ability to unblock yourself and request support
Tasks
- Contribute to the team roadmap
- Collaborate with product managers and engineers
- Identify opportunities and resource requirements
- Conceptualize and prototype anomaly detection systems
- Develop solutions for complex problems
- Communicate technical issues and results to team members
- Educate stakeholders about products and solutions
- Own the entire development cycle of algorithms
- Iteratively improve models and explore new data sources
- Build model pipelines and streamline analysis processes
Work Experience
- 3 years
Education
- Master's degree
Languages
- English – Business Fluent
Tools & Technologies
- Python
- Databricks
- Spark
Benefits
Flexible Working
- Hybrid working model
Workation & Sabbatical
- Work from abroad for 30 days
Competitive Pay
- Employee shares program
Corporate Discounts
- 40% off fashion products
- 30% off Lounge by Zalando
- Discounts from external partners
Other Benefits
- 2 paid volunteering days
- Health and wellbeing options
More Vacation Days
- 27 days of vacation
Additional Allowances
- Relocation assistance
Family Support
- Family services
Mental Health Support
- Mental health support
Learning & Development
- Training platform access
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Applied Scientist - Merchandise Integrity(m/w/x)
Prototyping anomaly detection systems for product data in an exclusive online shopping club. 3+ years industry experience developing anomaly detection and risk scoring required. 30 days work from abroad, employee shares program, 40% fashion discount.
Requirements
- Master's degree or PhD in data science, statistics, mathematics, physics, economics, or related quantitative discipline
- At least 3 years of industry experience in developing anomaly detection and risk scoring
- Extensive experience with Python and data science libraries
- Excellent verbal and written communication skills
- Curious and self-starter mindset
- Ability to unblock yourself and request support
Tasks
- Contribute to the team roadmap
- Collaborate with product managers and engineers
- Identify opportunities and resource requirements
- Conceptualize and prototype anomaly detection systems
- Develop solutions for complex problems
- Communicate technical issues and results to team members
- Educate stakeholders about products and solutions
- Own the entire development cycle of algorithms
- Iteratively improve models and explore new data sources
- Build model pipelines and streamline analysis processes
Work Experience
- 3 years
Education
- Master's degree
Languages
- English – Business Fluent
Tools & Technologies
- Python
- Databricks
- Spark
Benefits
Flexible Working
- Hybrid working model
Workation & Sabbatical
- Work from abroad for 30 days
Competitive Pay
- Employee shares program
Corporate Discounts
- 40% off fashion products
- 30% off Lounge by Zalando
- Discounts from external partners
Other Benefits
- 2 paid volunteering days
- Health and wellbeing options
More Vacation Days
- 27 days of vacation
Additional Allowances
- Relocation assistance
Family Support
- Family services
Mental Health Support
- Mental health support
Learning & Development
- Training platform access
Like this job?
BetaYour Career Agent finds similar jobs for you every day.
About the Company
Zalando
Industry
Retail
Description
The company is an exclusive online shopping club for fashion and lifestyle products across Europe, offering daily sales campaigns.
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