Data Scientist at ZALORA Group in Kuala Lumpur, Federal Territory of Kuala Lumpur, Malaysia

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Job Description

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  Job Responsibilities:
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   Drive the full lifecycle of Data Science/Analytics projects: from gathering and understanding the end-user needs to implement a fully automated solution.
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   Develop and provision of Data pipelines to enable self-service reports and dashboards.
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   Deploy Machine learning techniques to answer the appropriate business problems using R or Python.
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   Visualise data using Tableau and create repeatable visual analysis for end users to use as tools.
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   Take ownership of the existing BI platforms and maintain the data integrity and accuracy of the numbers and data sources.
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   Know Agile - Scrum project management experience/knowledge - Ability to prioritise, pushback and effectively manage a data product and sprint backlog.
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  Requirements:
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   4+years of professional data science or product analytics experience focused on empirical analytics, data mining, and predictive analytics to develop measurable insights.
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   Strong proficiency in writing production-quality code preferable in R/Python, engineering experience with machine learning projects like time series forecasting, Classification and optimisation problems.
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   Experience with Tableau, Power BI, Superset or any standard data visualisation tools.
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   Experience in building data pipelines using MPP databases (e.g. Redshift, BigQuery) and Google Analytics/Google Tag Manager is a bonus.
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   Management experience would be an added advantage point.
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   Exhibits sound business judgment, a proven ability to influence others, strong analytical skills, and a proven track record of taking ownership, leading data-driven analyses, and influencing results
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   E-commerce / logistics / fashion retail background a bonus.
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   Experience on GCP
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  Wonder how it's like to build your career with ZALORA? Inspired by employees, we believe you'll be in for:
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   An exciting platform to make your success story
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   Have the utmost care for your mental and physical wellbeing
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   Flexibility weaved into your lifestyle
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   A seamless work environment with a friendly &amp; team-fueled culture
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   Career growth aligned to your professional and personal needs and goals
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 Want to stay ahead with our developments? Follow us at our
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  The ZALORA Story
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 ZALORA is Asia&rsquo;s leading online fashion, beauty and lifestyle destination, part of Global Fashion Group. As one of the region&rsquo;s pioneer large scale eCommerce platforms, ZALORA has established a strong presence throughout the region, particularly in Singapore, Indonesia, Malaysia, Brunei, the Philippines, Hong Kong, and in Taiwan, enjoying over 50 million visits per month.
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  ZALORA is not obligated to accept resumes from any third parties on behalf of potential candidates for any position (advertised or otherwise) by any means unless ZALORA has executed a written agreement with such third party and has expressly requested such third party for candidate referrals. Third parties who provide unsolicited resumes of candidate(s) shall waive and forfeit all rights to claim for any placement fees or referral fees in the event that such candidate is eventually engaged or employed by ZALORA or Global Fashion Group.
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AI Powered Job Insights

Data Scientist Opportunity at ZALORA Group! They are on the lookout for a talented data scientist to take ownership of data-driven projects in their Kuala Lumpur office, making significant contributions in a fast-paced e-commerce environment.

📍 Location: Kuala Lumpur, Federal Territory of Kuala Lumpur, Malaysia  
💼 Position: Data Scientist  
⏰ Type: Full-time  
📅 Date Posted: 2024-07-01  

Role Summary:  
- Drive all phases of Data Science and Analytics projects from inception to execution.  
- Develop data pipelines for self-service reporting and dashboarding.  
- Apply machine learning techniques using R or Python to solve business challenges.  
- Visualize data with tools like Tableau for easy understanding and accessibility.  
- Maintain the integrity and accuracy of existing BI platforms.  
- Utilize Agile-Scrum experiences to manage projects efficiently.  

What You'll Do:  
- Gather user requirements to create automated solutions.  
- Build and manage reliable data pipelines with MPP databases.  
- Work on empirical analytics and predictive modeling to generate actionable insights.  
- Collaborate on data visualization efforts to empower end users.  
- Influence project outcomes and maintain data quality across departments.  

What's Needed:  
- 4+ years of experience in data science/product analytics.  
- Strong coding skills in R or Python for production-quality code.  
- Experience with data visualization tools like Tableau or Power BI.  
- Knowledge of GCP and building data pipelines is advantageous.  
- E-commerce, logistics, or fashion retail background as a bonus.  
- Ability to influence and lead data-driven decisions.  

ZALORA offers a vibrant work environment, emphasizing career growth, flexibility, and employee well-being. This could be the perfect chance for those looking to expand their career in the e-commerce and fashion retail space!

Top Interview Questions

  • Q: Can you describe your experience with end-to-end data science project lifecycles and provide an example?

    A: In my previous role, I led the full lifecycle of a project to predict customer churn in an e-commerce platform. I started by gathering user requirements through stakeholder interviews and surveys. I then cleaned and transformed the data using Python, built predictive models using machine learning techniques like logistic regression and random forests, and finally visualized the results in Tableau, providing actionable insights to the marketing team. This project not only improved customer retention strategies but also reduced churn by 15%.

  • Q: What methodologies do you employ to ensure data integrity and accuracy in business intelligence platforms?

    A: To maintain data integrity, I implement rigorous data validation checks at multiple stages. I use automated testing to compare incoming data against expected patterns and values. Additionally, I regular audits to spot discrepancies early. I also ensure that documentation is up-to-date, which provides transparency and aids in troubleshooting. For instance, in a recent project, I tracked the accuracy of daily sales reports and found a recurrent data entry error, which, once rectified, improved our reporting accuracy to over 98%.

  • Q: How do you prioritize tasks within an Agile-Scrum framework while working on data products?

    A: I prioritize tasks by collaborating closely with the product manager and stakeholders during sprint planning sessions. I assess the business value of each task, align it with project goals, and use techniques like the MoSCoW method to categorize tasks as Must have, Should have, Could have, or Won't have. For instance, in my last sprint, we prioritized building a data pipeline that automated weekly reports over less urgent features, enabling the team to focus on high-impact delivery first.

  • Q: Discuss a time when you used a machine learning technique to solve a business problem. What was the approach and outcome?

    A: I worked on a project that involved forecasting inventory needs for a fashion retail client. I implemented time series forecasting using ARIMA and seasonal decomposition. After analyzing historical sales data and external factors like seasonal trends, I created a forecasting model that improved inventory accuracy by 30%. This predictive insight allowed the client to optimize stock levels, reducing costs associated with overstock and stockouts.

  • Q: What is your approach to visualizing complex data, and how do you ensure your dashboards are user-friendly for stakeholders?

    A: My approach to visualizing complex data involves understanding the audience's needs first. I conduct brief discussions to learn about key metrics and decision-making criteria. I then design dashboards using Tableau, focusing on simplicity and clarity, incorporating intuitive layouts, and using color effectively to highlight important information. For example, I created an executive dashboard that summarized sales performance with drill-down capabilities, allowing stakeholders to quickly gain insights from high-level overviews to detailed analyses.

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