Bilingual Data Scientist

[Job Description] Senior Data Scientists leverage advanced analytics, machine learning, and statistical modeling to tackle complex business challenges in a data-driven manner. This role requires high expertise in translating data insights into actionable strategies and strong communication skills to collaborate effectively across departmental teams. Candidates with extensive experience in analytics, the IT/tech industry, or cutting-edge technologies are welcome. [Specific Responsibilities] * Optimization of Ad Message Delivery: Develop and operate machine learning (ML) models to optimize the content and timing of personalized ads for maximum results, predict customer lifetime value (CLV), perform multi-channel customer modeling and segmentation, and extract insights from delivery results. * Personalization & Recommendation: Design and implement recommendation engines utilizing various methods such as rule-based, machine learning, and deep learning to improve user engagement and brand trust. * Advanced Customer Segmentation: Lead customer behavior analysis using clustering techniques to drive brand enhancement strategies optimized for each individual customer. * Data Engineering & Exploratory Data Analysis (EDA): We perform cleansing, preprocessing, and validation of large and complex datasets (structured and unstructured data). We also identify patterns within the data through exploratory data analysis (EDA), ensuring data quality before formulating initial analysis strategies. * KPI Design and Stakeholder Proposals: We collaborate with relevant internal departments (sales, customer success, etc.) and client marketing teams to define and propose business KPIs that are logical, ambitious, and easily understandable to non-specialist teams. * Building a Continuous Improvement Loop: Through multifaceted verification and error analysis, we systematically extract and identify model accuracy issues, continuously refining (iterating) the model structure and features to achieve target KPIs. * Collaborative Development Process and Privacy Considerations: We manage production-level code repositories using GitHub, thoroughly implementing version control and documentation from the research and development stage. We also prioritize data privacy protection and AI ethics in our development process. * Research and application of cutting-edge technologies: We research and implement cutting-edge technologies such as deep learning, natural language processing (NLP), LLM, and generative AI to drive problem-solving aimed at enhancing business strategies and improving brand value. * Assigned Team: Data & AI Team * Expectations for this position: * Lead the entire ML lifecycle, from data exploration to model implementation, operation, and maintenance, in core algorithms such as recommendation engines and ad delivery optimization. * Continuously improve the accuracy and value of models in a way that directly links to business KPIs, and impact sales and business growth through data-driven approaches. * Collaborate with management and business stakeholders, translate business challenges into data-driven approaches, and lead everything from KPI design to policy proposals. * Maximize overall team performance through the development of junior members in both technical and non-technical aspects, and the establishment of development standards and documentation culture. Working Hours 9:00 AM - 6:00 PM (including a 1-hour break; overtime work may be required) Flextime system (core time 10:00 AM - 4:00 PM) *According to company regulations, discretionary work arrangements apply to employees above a certain grade level. Job Requirements [Required Skills] Four+ years of practical experience in the field of data science, and high level of expertise in the following areas: * Expertise and practical experience in machine learning and statistics * Expertise in fundamental machine learning theory (prediction, classification, regression, clustering) and statistics for tabular and time series data. Practical experience solving business problems in these areas using major libraries such as Numpy, Scikit-learn, and PyTorch. * Programming & Code Quality * Proficiency in Python and SQL. Ability to code with an awareness of code quality, readability, and maintainability. * Analysis & Validation Methods * Skills in the iterative process of hypothesis testing and offline evaluation (model accuracy validation) using methods such as cross-validation. Practical knowledge of A/B testing and basic statistical analysis methods. * Leading the entire ML lifecycle (End-to-End) * Experience in the entire ML development process, from data exploration, feature engineering, KPI definition, model training and validation, system implementation, performance evaluation, and operation and maintenance of ML systems in a production environment. Development Environment & Team Collaboration: * Proficiency in Linux environments, VS Code, version control (Git/GitHub), containerization (Docker, etc.), and CI/CD (GitHub Actions, etc.). Experience in collaborating with engineering teams to integrate ML models into production environments. Language Skills: * Japanese: Business level or higher (including not only everyday conversation but also smooth communication in business-related conversation, writing, and reading comprehension). * English: No psychological resistance to technical conversations and a willingness to catch up (conversation, writing, and reading comprehension). [Preferred Skills] * Data and Analysis Tools * Practical knowledge of SQL databases (MySQL, PostgreSQL, etc.), data warehouses, and OLAP (Snowflake, Redshift, etc.). * Cloud Platforms * Practical experience in major cloud environments (AWS, Azure, GCP) (primarily AWS). * Deep expertise in building recommendation systems * Practical knowledge and experience in model building using collaborative filtering, content-based filtering, matrix factorization, NCF (Neural Collaborative Filtering), and two-tower models. * Advanced ML Engineering * Experience and knowledge of online inference (Online Serving), model deployment, ML workflows in production environments (feature pipeline construction, monitoring, model retraining/improvement cycles, etc.), and MLOps. * Experience in designing and developing microservice architectures (e.g., FastAPI, Flask) * Ability to perform fine-tuning of open-source deep learning models (e.g., optimization and additional learning in embedding representations, sequence models, multi-task learning, etc.) * Experience in developing and operating high-QPS (Query Per Second) and low-latency machine learning APIs. * Data Engineering & Orchestration * Practical experience with workflow/DAG management tools (Apache Airflow, AWS Step Functions, etc.) for building and operating scalable data pipelines. * Development and operation experience utilizing streaming and messaging technologies (AWS Kinesis, Apache Kafka, etc.). * Practical experience using distributed learning environments and large-scale data processing frameworks (Apache Hadoop, Apache Spark, MPI, etc.). * Domain expertise and business understanding/insight. * Project experience in related areas such as ranking, search, advertising delivery, and personalization systems. * Ability to propose data analysis and improvement measures based on a deep understanding of business objectives and domain knowledge. * Understanding of user behavior analysis, business growth indicators, and optimization from a business perspective. * Community activities and research achievements. * High motivation for applied machine learning and supporting activity records, including contributions to OSS (open source) projects, publication of research papers, and participation in competitions such as Kaggle. [Desired Candidate Profile] - Ability to smoothly reach agreements with internal and external stakeholders and collaborate across departments - Ability to break down complex data analysis results into easily understandable stories for non-technical individuals - Ability to translate business challenges into data-driven approaches and persistently work towards problem-solving - Possess professional time management skills and the ability to proactively drive work forward - Motivation to develop junior members from both technical and non-technical perspectives and achieve results as a team - High motivation to keep up with and implement cutting-edge AI and data technologies English Level: Business Conversation Level (TOEIC 735-860) Japanese Level: Business Level(JLPT Level 2 or N2) Benefits - Transportation expenses paid (bicycle commuting permitted) - Company-provided laptop - Company-provided mobile phone (for some positions only) - Social insurance (health insurance, employee pension, employment insurance, workers' compensation insurance) - Life and health support leave - Health checkup cost reimbursement - Childbirth and childcare support system - Club activities - Book purchase support (¥55,000 per year) - Subsidies for customer service use - Subsidies for departmental social gatherings Holidays • Saturdays, Sundays, and public holidays • Year-end and New Year holidays • Summer holidays • Bereavement leave • Paid leave (10 days granted upon joining the company, and subsequently granted according to length of service) *Other holidays as per company regulations Job Contract Period Full-time employee Trial period: Generally 3 months *There will be no change in treatment during the trial period.