Junior Data Scientist

Position: Junior Data Scientist
Company: Retentics
Location: Seoul
Employment type: Full-time
Skills: Python, SQL
Posted: 2026-06-26

── About this role ──
As a Data Scientist at Retentics, 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:

• 1+ 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

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Source: Dev Korea