Data Science Wfo; Bantul, Diy

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Head of Data Science is responsible to transform our vast telecommunications datasets into high-value commercial assets. This role is unique: you will lead the end‑to‑end development of proprietary data products (Monetization) while simultaneously building a world‑class professional services arm to help external clients solve complex problems through predictive modelling (Consulting).

Data Monetization & Product Strategy

• Productize Telco Data:
  Identify, design, and launch data products (e.g., mobility insights, churn prediction, foot‑traffic analytics) using anonymized subscriber data.
• Privacy‑First Innovation:
  Ensure all products adhere to strict data privacy regulations through robust anonymization and differential privacy techniques.
• Tech Stack Ownership:
  Oversee the architecture for high‑scale data processing and model deployment.

Consulting & Client Services

• External Advisory:
  Act as a lead consultant for external B2B clients, helping them identify high‑impact AI use cases and roadmap their data journeys.
• Custom Model Development:
  Lead a team to build, validate, and deploy bespoke predictive models for external partners across various industries.
• Pre‑sales Support:
  Partner with the sales team to provide technical validation during the pitching process for consulting engagements.

Leadership & Talent Growth

• Team Building:
  Recruit and mentor a team of data scientists.
• Standardization:
  Establish Gold Standards for code quality, model reproducibility, and documentation across both internal and external projects.

Requirements

• 8+ years of experience in data science with 2+ years of experience managing a team.
• Deep knowledge of Python, GCP, end‑to‑end machine learning frameworks including MLOps.
• Proven track record in telecommunications or data monetization.
• Prior experience in a client‑facing consulting role.
• Able to communicate technical results and insights to non‑technical stakeholders clearly and concisely.
• Excellent problem‑solving skills and ability to work independently and as part of a cross‑functional team.
• Ownership mindset; customer insights are the foundation of the squad.
• Data‑driven; pushes others to maintain an impact lens.

This role focuses on designing, building, and deploying machine learning models and systems that drive intelligent solutions.

Responsibilities

• Develop, train, and deploy machine learning models using various algorithms and techniques.
• Build and maintain robust ML pipelines for data processing, feature engineering, and model evaluation.
• Collaborate with data scientists to translate research models into production‑ready code.
• Optimize ML models for performance, scalability, and efficiency.
• Implement and manage MLOps practices for continuous integration, delivery, and monitoring of ML systems.
• Work with big data technologies and cloud platforms.
• Troubleshoot and debug ML models and systems in production.
• Stay current with the latest advancements in machine learning and AI.
• Document ML processes, models, and results.
• Contribute to the overall AI strategy and roadmap.

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Statistics, or a related quantitative field.
• Proven experience as a Machine Learning Engineer or in a similar role.
• Strong programming skills in Python and proficiency with ML libraries (Tensor Flow, PyTorch, scikit‑learn).
• Experience with data manipulation and analysis libraries (Pandas, Num Py).
• Familiarity with cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).
• Understanding of software engineering best practices, including version control (Git) and CI/CD.
• Experience with data warehousing and big data technologies is a plus.
• Excellent problem‑solving and analytical skills.
• Effective communication and teamwork abilities.
• Ability to balance remote work with required on‑site collaboration.

Program highlights:

• Intensive training in statistical modeling, machine learning, data visualization, and programming languages (Python, R).
• Exposure to data…