Sr. Data Scientist

PT Amman Mineral Nusa Tenggara; Nusa Tenggara Barat; Presencial; IDR 400.000.000 - 700.000.000; Jornada completa

Descripción de la vacante

PT Amman Mineral Nusa Tenggara seeks a senior data scientist to design analytics and machine-learning solutions for the Mining product team at site-based West Sumbawa. You will frame problems with operations, develop models, and deploy them to production, collaborating with Digital Engineering and Change teams.

You will use AI agents to speed up analysis while keeping human judgement over data and model use. Bahasa Indonesia and English communication are required.

Formación

• Minimum Bachelor's degree in a quantitative field (data science, statistics, CS, engineering or mining).
• 8+ years in applied data science and ML with production-grade models.
• Experience with mining or asset-intensive data and framing problems with operations.

Responsabilidades

• Analyse Mining problems with operations and Product Manager to define measurable outcomes.
• Build and deploy features and ML/optimisation models for production use.
• Run models in operation, monitor accuracy/drift, and retrain as needed.
• Build capability by sharing standards and guiding supplier data scientists.

Conocimientos

Python

SQL

ML/AI modeling

Operations framing

Exploratory data analysis

Bilingual communication

Educación

Bachelor's degree in Data Science, Statistics, CS, Engineering or Mining

Herramientas

MLOps

Descripción del empleo

Working Location: Site Based - West Sumbawa

Sr. Data Scientist builds the analytical and machine-learning solutions that help the Mining product team solve its priority problems, such as fleet and mine-plan performance, equipment reliability and grade control. The role frames problems with mining operations, analyses the data, and builds, validates and deploys models that run in production.

The role is part of the Mining squad and reports to the Manager - Digital Product. It follows the modelling standards of the data science and machine-learning centre of excellence and the engineering standards set by the Lead Digital Engineering, and works with the squad's Solution Architect, Digital Delivery and Digital Change & Adoption.

The role uses AI agents to accelerate analysis, feature engineering and evaluation, and keeps human judgement over the data, the assumptions and the model's use in the operation.

Some of your duties will include:

Analyse Mining problems

• Frame priority Mining problems with operations and the Product Manager, and define the outcome a solution must improve.
• Analyse fleet, geological, production and sensor data to find causes, opportunities and measurable value.
• Present findings and options clearly to operational leaders.

Build and deploy models

• Engineer features, and select, train and validate statistical, machine-learning and optimisation models.
• Design the experiments and tests that show whether a solution works before it reaches the operation.
• Deploy models into production with Platforms & Data, using the centre of excellence's MLOps practice.

Run and improve models in operation

• Monitor live models for accuracy and drift, and retrain or retire them as the operation changes.
• Work with the Change & Adoption Lead so solutions are used in daily operations.
• Measure the value delivered and report it to the Product Manager.

Build capability

• Apply the centre of excellence's modelling standards and contribute reusable approaches across domains.
• Guide and review the work of supplier data scientists in the Mining squad.
• Use AI agents to speed up analysis, code and evaluation, with human review.

What skills and experience do you need?

• Minimum Bachelor's degree in Data Science, Statistics, Computer Science, Engineering, Mining Engineering or another quantitative discipline. A relevant postgraduate qualification is preferred
• Minimum 8 years in applied data science and machine learning, with strong Python, including models that run in production and are monitored once live.
• Experience with mining, fleet, geological, sensor or operational data in mining or another asset-intensive industry is strongly preferred, as is experience framing problems directly with operations.

Technical Competencies in:

• Programming: strong Python and SQL for data science.
• Machine learning, statistics and optimisation: feature engineering, model selection, training and validation.
• Mining and operational data: fleet management, dispatch, drill and blast, geology, maintenance and sensor data, and their limits.
• Experiment design and value measurement against operational outcomes.
• MLOps: deployment, experiment tracking, monitoring for accuracy and drift, and retraining.
• Responsible AI: validating models for bias, reliability, safety and fit before production.
• AI-augmented practice with human judgement held over the data and conclusions.
• Clear written and verbal communication in Bahasa Indonesia and English