About the role
structured by ORIKey Responsibilities Data Analysis & Insights: Analyze large datasets from multiple sources (clickstream, sales, user engagement) to uncover insights and support business decision-making. Machine Learning: Develop, deploy, and maintain machine learning models for recommendations, personalization, customer…
What you will do
- Data Analysis & Insights: Analyze large datasets from multiple sources (clickstream, sales, user engagement) to uncover insights and support business decision-making.
- Machine Learning: Develop, deploy, and maintain machine learning models for recommendations, personalization, customer segmentation, demand forecasting, and pricing.
- A/B Testing: Design and analyze A/B tests to evaluate the performance of product features, marketing campaigns, and user experiences.
- Data Pipeline Development: Work closely with data engineering teams to ensure the availability of accurate and timely data for analysis.
- Collaborate with Cross-functional Teams: Partner with product, marketing, and engineering teams to deliver actionable insights and improve platform performance.
What they are looking for
- Proficiency in Python or R: Experience in using Python or R for data analysis and machine learning.
- SQL: Strong SQL skills to query and manipulate large datasets.
- Machine Learning Frameworks: Familiarity with libraries such as Scikit-learn, TensorFlow, or PyTorch.
- Data Wrangling: Ability to clean, organize, and manipulate data from various sources.
- A/B Testing: Experience designing experiments and interpreting test results.
- Visualization Tools: Proficiency with data visualization tools (e.g., Tableau, Power BI, Matplotlib, Seaborn).
- Statistics & Probability: Strong grasp of statistical methods, hypothesis testing, and probability theory.
- Communication: Ability to convey complex findings in clear, simple terms for non-technical stakeholders.
Nice to have
- E-commerce Experience: Experience working with e-commerce datasets (e.g., user behavior, transaction data, inventory, and product data).
- Experience with Big Data: Familiarity with big data technologies (e.g., Hadoop, Spark, BigQuery).
- Cloud Platforms: Experience with cloud platforms like AWS, GCP, or Azure for data storage and model deployment.
- Business Acumen: Understanding of key business metrics in e-commerce, such as conversion rates, LTV, and customer acquisition cost (CAC).
Full posting text
Key Responsibilities
Data Analysis & Insights: Analyze large datasets from multiple sources (clickstream, sales, user engagement) to uncover insights and support business decision-making.
Machine Learning: Develop, deploy, and maintain machine learning models for recommendations, personalization, customer segmentation, demand forecasting, and pricing.
A/B Testing: Design and analyze A/B tests to evaluate the performance of product features, marketing campaigns, and user experiences.
Data Pipeline Development: Work closely with data engineering teams to ensure the availability of accurate and timely data for analysis.
Collaborate with Cross-functional Teams: Partner with product, marketing, and engineering teams to deliver actionable insights and improve platform performance.
Data Visualization: Build dashboards and visualizations to communicate findings to stakeholders in an understandable and impactful way.
Exploratory Analysis: Identify trends, patterns, and outliers to help guide business strategy and performance improvements.
Required Skills:
Proficiency in Python or R: Experience in using Python or R for data analysis and machine learning.
SQL: Strong SQL skills to query and manipulate large datasets.
Machine Learning Frameworks: Familiarity with libraries such as Scikit-learn, TensorFlow, or PyTorch.
Data Wrangling: Ability to clean, organize, and manipulate data from various sources.
A/B Testing: Experience designing experiments and interpreting test results.
Visualization Tools: Proficiency with data visualization tools (e.g., Tableau, Power BI, Matplotlib, Seaborn).
Statistics & Probability: Strong grasp of statistical methods, hypothesis testing, and probability theory.
Communication: Ability to convey complex findings in clear, simple terms for non-technical stakeholders.
Preferred Qualifications:
E-commerce Experience: Experience working with e-commerce datasets (e.g., user behavior, transaction data, inventory, and product data).
Experience with Big Data: Familiarity with big data technologies (e.g., Hadoop, Spark, BigQuery).
Cloud Platforms: Experience with cloud platforms like AWS, GCP, or Azure for data storage and model deployment.
Business Acumen: Understanding of key business metrics in e-commerce, such as conversion rates, LTV, and customer acquisition cost (CAC).
Education:
Bachelor’s or Master’s degree in Data Science, Computer Science, Mathematics, Statistics, or related field.
Seniority level: Mid-Senior level
Employment type: Full-time
Job function: Engineering and Information Technology
Industries: Technology, Information and Internet