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Acuerdo de trabajo
Tiempo completo
Esquema de Trabajo
Híbrido
Asistencia de reubicación disponible
Sí
Fecha de publicación
23-may-2025
ID del trabajo
9440

Descripción y requisitos

Position Overview:
The Lead Data Scientist will drive innovative approaches to understanding, interacting, and optimizing customer engagement by leveraging advanced analytics to derive actionable business insights. The successful candidate will work closely with business stakeholders, primarily in Japanese, to understand their requirements and the broader business context. This role entails leading data analytics initiatives from conception through implementation to delivery.

Responsibilities:
• Collaborate with business stakeholders to gather requirements, understand business context, and derive data-driven solutions. 
• Lead and oversee data analytics initiatives, including planning, execution, and delivery of high-quality analytical solutions. 
• Research, propose, and develop state-of-the-art AI/ML solutions that address complex business challenges, keeping pace with the rapidly evolving AI landscape. 
• Rapidly prototype and conduct Proofs of Concept (PoCs) to validate innovative approaches and demonstrate their business value before full implementation. 
• Design and implement enterprise-grade Generative AI applications leveraging Large Language Models (LLMs), Retrieval Augmented Generation (RAG), and multi-agent frameworks. 
• Work closely with the data platform team and other IT teams, identifying dependencies, and resolving blockers to ensure smooth development processes. 
• Develop, deploy, and manage machine learning models and analytics projects, ensuring alignment with business objectives. 
• Utilize CI/CD, MLOps, Azure or similar cloud platforms, and DevOps to streamline development and deployment processes. 
• Apply hands-on expertise with SQL, Python, Pandas, PySpark, AzureML, and PowerBI to develop and implement data models and analytics. 
• Leverage AI-assisted development tools such as GitHub Copilot to enhance programming efficiency and code quality. 
• Leverage domain expertise in financial or insurance sectors, especially in distribution, operations and marketing, to ensure successful project outcomes. 
• Prepare detailed documentation for analytics and ML initiatives, including project deliverables and technical specifications. 
• Communicate effectively with team members and leadership through presentations and reports detailing analytics and machine learning requirements, progress, and outcomes. 
• Work closely with the data platform team and other IT teams, identifying dependencies and resolving blockers to ensure smooth development processes. 
• Ensure the responsible use of AI practices, adhering to ethical guidelines and compliance standards. 
• Employ best practices and methodologies from other markets to improve analytics and data solutions. 
• Proactively propose new solutions and enhancements based on emerging technologies and business needs. 
• Evaluate and recommend approaches for fine-tuning and deploying foundation models to solve domain-specific problems.

Requirements:
Experience:
• 5+ years of experience in advanced analytics roles, delivering successful business outcomes. 
• Proven leadership experience in managing data science or analytics teams. 
• Minimum 2+ years of hands-on experience implementing Generative AI solutions in enterprise settings.

Technical Skills:
• Proficiency in quantitative methods and business analytics with strong problem-solving skills. 
• Expertise in building and applying data models using machine learning techniques such as classification, regression, clustering, time-series analysis, and dimensionality reduction. 
• Competent programming skills with Python and SQL. 
• Strong understanding of data visualization tools, particularly PowerBI. 
• Experience with CI/CD, MLOps, and cloud platforms such as Azure for deploying analytics and ML solutions. 
• Hands-on experience with data analytics tools and libraries such as Pandas, PySpark, and AzureML. 
• Demonstrated expertise in implementing and optimizing Large Language Models (LLMs) and Generative AI applications. 
• Hands-on experience with Retrieval Augmented Generation (RAG) architectures, vector databases, and semantic search systems. 
• Practical knowledge of orchestration frameworks such as Langchain, Langsmith, and Langraph for building multi-agent AI systems. 
• Experience using AI-assisted development tools like GitHub Copilot to enhance coding productivity and quality. 
• Track record of successfully delivering rapid prototypes and PoCs that demonstrate business value of advanced AI techniques. 
• Background in financial or insurance domains, particularly in distribution, operations and marketing. 
• Continuous learning of new tools and technologies to stay ahead of industry trends and enhance analytics capabilities.

Leadership Competencies:
• Continuous motivation to upgrade domain knowledge and evaluate its business application. 
• Strong interpersonal skills to build relationships, rapport, and influence colleagues at all levels. 
• Excellent communication skills to present and convey complex information concisely to diverse audiences. 
• Ability to translate cutting-edge AI capabilities into business-relevant solutions and communicate their value to stakeholders.

Education:
• Bachelor's degree in a quantitative modeling-focused discipline such as Statistics, Marketing Science, Operations Research, Econometrics, Machine Learning, or a related field. 
• An advanced degree in similar disciplines is a plus. Courses or specialization in data & analytics. 
• Azure certifications, especially Azure Data Scientist, Azure Enterprise Data Analyst, Azure Data Engineer. 
• Certifications or specialized training in GenAI, LLMs, or advanced AI frameworks is highly desirable.

Language Requirements:
• Japanese: Advanced 
• English: Intermediate

Additional Information:
The role requires a proactive leader with a strong analytical background, capable of driving data initiatives while collaborating effectively with cross-functional teams. The ideal candidate will have financial or insurance expertise, technical prowess with relevant tools, and the ability to navigate complex business environments to deliver impactful analytics solutions. They will also be at the forefront of implementing cutting-edge AI technologies, including GenAI and LLMs, to solve complex business problems through rapid prototyping and innovative solution design.