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Data Scientist
Seoul (On-site) • Full-time
- Python
- SQL
- Data Analysis
Insights about this position
Visa sponsorship: No
The company cannot sponsor a Korean work visa for this role.
Korean language proficiency: Not required
No knowledge of Korean is required for this role.
Workplace type: On-site
Work is primarily performed at the office.
As a Data Scientist at Retentix, you will design and refine predictive models and recommendation algorithms that form the core of our AI-powered email marketing SaaS product. You will play a key role in driving repeat purchases and LTV growth for our diverse DTC brand clients through data-driven solutions. You will translate business problems into data problems and work closely with backend engineers to ensure models operate reliably and efficiently in production environments.
Key Responsibilities
- Design and operate customer segmentation, targeting, and product/content recommendation algorithms to drive repeat purchases
- Develop and improve predictive models (e.g., repurchase timing, response probability) based on customer behavior data
- Collaborate with backend engineers to optimize ML model serving, inference speed, and operational stability
- Continuously improve model performance based on experiment results and translate findings into measurable business impact
Requirements
- 3+ years of hands-on project experience in ML/DL modeling
- Experience in personalization recommendation and customer behavior prediction modeling
- Understanding of or work experience in e-commerce and transactional data domains
- Proficiency in Python and SQL programming
- Solid understanding of how ML/DL models work and their underlying mathematical principles
- Experience leading end-to-end projects from problem definition to model deployment
- Strong communication skills to clearly explain analytical results and model architecture to non-technical stakeholders
Preferred Qualifications
- Master's degree or higher in a Data Science-related field (Statistics, Mathematics, Computer Science, etc.)
- Experience in ML modeling within a B2C business environment
- Experience with large-scale data processing and analysis (Spark)
- Comfortable with rapid experimentation and iterative model improvement through feedback cycles
- Degree obtained in an English-speaking country, or ability to work in English
Tech Stack
- Data processing/analysis libraries: NumPy, Pandas, Polars, Spark
- ML/DL libraries: Scikit-Learn, PyTorch, TensorFlow
- Cloud computing services: AWS