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Retentics& #39; Data Scientist plays a core role in our AI-based email marketing SaaS product — designing and refining predictive models and recommendation algorithms that turn repurchase behavior and LTV growth across diverse DTC brand clients into data-driven outcomes. You& #39;ll define business problems as data problems, and work closely with backend engineers to implement and optimize models so they run stably in a production environment.
[Key Responsibilities]
- Design and operate customer segmentation, targeting, and product/content recommendation algorithms to drive customer repurchase
- Develop and refine predictive models based on customer behavior data (repurchase timing, response probability, etc.)
- Collaborate with backend engineers to optimize ML model serving and training speed, and ensure operational stability
- Continuously improve model performance based on experimental results and connect findings to business impact
- 직업요구사항
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[We& #39;re looking for a Data Scientist to grow with Retentics]
- 3~ 5+ 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 the e-commerce and transaction data domain
- Proficiency in Python and SQL
- Understanding of ML/DL operating principles and mathematical foundations
- Experience leading end-to-end processes from problem definition to model deployment
- Ability to clearly communicate analytical results and model structures to non-technical stakeholders
[Preferred Qualifications - Nice to have:]
- Master& #39;s degree or higher in a Data Science-related field (Statistics, Mathematics, Computer Science, etc.)
- ML modeling experience in a B2C business environment
- Experience with large-scale data processing/analysis using Spark
- Ability to iterate quickly and improve models through rapid experimentation
- Degree obtained in an English-speaking country, or ability to work in English
[Data Scientist Tech Stack]
- Data processing/analysis libraries: NumPy, Pandas, Polars, Spark
- ML/DL libraries: Scikit-Learn, PyTorch, TensorFlow
- Cloud computing: AWS
- 지원지침
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[Hiring Process]
Document Review → 1st Interview (Skill Set) → 2nd Interview (Culture Fit) → Reference Check → Final Off
취업 담당자
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Lea Jang
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전화번호
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010-5328-6996
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이메일
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jooyeon@fridayslab.com
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