Data Sience & AI Manager

About OTA Indonesia (OTAZEN)

Founded in the early nineties, OTAZEN has grown into a world-class leader in the furniture manufacturing and logistics industry, exporting high-quality products across five continents. We cater to diverse sectors, including luxury hotels, domestic markets, and large-scale commercial contracts.

Operating out of our own advanced, independent production facilities equipped with cutting-edge woodworking technology, we pride ourselves on being completely self-sufficient and independent from external manufacturers. Our production processes adhere strictly to European quality standards and global sustainability initiatives, backed by annual FSC, COC, and SVLK certifications. At OTAZEN, we are our own most demanding customer, constantly creating and implementing global, innovative solutions to accelerate our business and support our international clients.

Job Overview

As we accelerate our digital transformation journey, we are looking for an entrepreneurial Data Science & AI Manager to act as the Founding Leader of our newly established Data & AI division.

Because this is a "Zero-to-One" phase, you will initially operate as a highly technical Individual Contributor during the first 6–12 months. You will be responsible for getting your hands dirty—writing code, building data pipelines, and deploying production-ready AI solutions to optimize our manufacturing, supply chain, and enterprise workflows. As your solutions scale and drive measurable business impact, you will have the full authority to design the hiring roadmap, manage the budget, and build your own Data Science and AI team from the ground up.

Key Responsibilities

• Hands-on AI & Data Development: Architect, build, and deploy the company’s very first production-grade AI models, RAG systems, and data pipelines from scratch.

• AI Strategy & Use-Case Identification: Collaborate directly with the executive team (C-Level) to translate manufacturing, logistics, and business challenges into scalable AI solutions (e.g., supply chain optimization, predictive maintenance, automated contract analysis, or internal knowledge management).

• Modern AI Integration: Explore and implement advanced Generative AI frameworks, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic Workflows, alongside traditional Machine Learning models.

• End-to-End MLOps: Establish robust MLOps practices using cloud infrastructure to ensure all deployed models are cost-effective, secure, low-latency, and highly reliable.

• Team Expansion & Future Leadership: Define technical hiring criteria, conduct technical interviews, and gradually build a high-performing team of Data Scientists, AI Engineers, and Data Engineers as the division expands.

• Technical Bridge: Act as the primary communicator between complex technical architectures and business outcomes, presenting milestones to non-technical stakeholders clearly.

Requirements & Qualifications

Experience & Mindset:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Information Technology, Mathematics, Statistics, or a related field.

• Minimum of 5 years of professional experience in Data Science or AI Engineering, with a proven track record of shipping production-grade systems.

• A strong "Zero-to-One" mentality: Excited about working independently in the early stages, writing code, and deploying systems without relying on an existing team.

• Demonstrated potential or prior experience in technical project management, mentoring, or team leadership.

Technical Skills:

• Expert proficiency in Python and standard ML libraries (TensorFlow, PyTorch, Scikit-Learn, Pandas).

• Strong hands-on experience with Generative AI architectures: LLM fine-tuning, RAG frameworks (Vector & Knowledge Graph), LangGraph/LangChain, and Agentic AI.

• Proficiency in relational databases (SQL/PostgreSQL) and Vector Databases (e.g., Qdrant, Pinecone, Milvus).

• Solid understanding of cloud infrastructure (AWS or Google Cloud Platform) along with containerization and orchestration tools (Docker & Kubernetes) for deployment.

• Familiarity with data streaming, automated workflows (like n8n), or applying AI to industrial/logistics data is a significant plus.

Soft Skills:

• Exceptional problem-solving skills and resilience in a dynamic, evolving environment.

• High level of professional autonomy and excellent time management.

• Strong interpersonal and communication skills in English (both written and verbal) to collaborate with global stakeholders.