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Data Analyst at Eneba

The Elite Digest

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Job Overview

  • Date Posted
    December 11, 2023
  • Expiration date
  • Industry
  • Qualification
  • Career Level

Job Description

Behind every groundbreaking product, there’s an awesome team! In Product, we gather folks with diverse backgrounds and expertise in areas such as product development, management, design, QA, UX, analytics, and more. We move fast, experiment, learn a lot, and build new products for our growing base of 9+ million active users. At the same time, we continue to iterate and improve on our flagship products: Digital, a safe and affordable marketplace for gamers. Physical, where we enable users to turn their second-hand items into someone’s first-tier gaming experience. Or Payments & Risk, where we provide a smooth, secure and reliable purchase experience.
Collaborate with your product team to define key performance indicators (KPIs) for the product. Design and implement robust metric tracking systems, ensuring consistent and accurate data collection.
Design A/B tests and other experiments to validate product hypotheses. Analyze results to provide insights on product changes and their impact on user behavior.
Generate product insights from data analyses to suggest new features or improvements. Bridge the gap between data and product by translating findings into actionable product strategies.
Dive deep into the data to determine the root causes of observed patterns or anomalies.
Identify key drivers influencing metrics and advise on potential interventions.
Conduct ad-hoc analyses to answer specific business questions or to inform product strategy.
Create clear and impactful visualizations that convey complex data insights.
Collaborate with stakeholders to communicate findings, provide recommendations, and influence product direction.

Minimum Requirements
Strong proficiency in Python or R.
Demonstrated experience in SQL (experience with other data storage technologies is a plus)
Mastery of statistical hypothesis testing, experiment design, and causal inference techniques.
Hands-on experience with A/B and multivariate testing methodologies.
Familiarity with behavioral data and experimentation platforms such as Mixpanel, GrowthBook.
Strong visual, written, and verbal communication skills and ability to convey complex analytical results to non-technical audiences.

Preferred Requirements
Familiarity with ML algorithms and techniques, including but not limited to regression models, clustering, and classification.
Expertise in building, evaluating, and deploying ML models using libraries like scikit-learn, TensorFlow, or PyTorch.
Experience in e-commerce, marketplace business-models, proficiency in digital marketing.