Senior Data Scientist, Data Analytics

RGE Digital's mission is to enable the digital transformation of RGE's business units through the adoption of modern software technologies such as sensors, analytics engineering, cloud and artificial intelligence. Our business use cases span across forestry, paper manufacturing, edible oils, supply chain and renewable energy where we have global scale operations across four continents with USD 35 billion in assets and 70,000 employees.

We take a product-focused approach to providing business solutions, prototype ideas, test them in the real business scenarios, and build re-usable components. We are at the early innings of building a modern data tack and an advanced analytics capability that harnesses the vast data we possess but that is first-of-its-kind for our industry.

Position Summary

This role is responsible for turning operational data into clear, actionable insights that improve business performance. You will lead process and data analysis, design and build operational dashboards, and support the implementation of key digital initiatives that drive measurable impact on business metrics.

You will also play a key role in raising the overall analytics capability of the team and wider organization: setting standards for good analysis and dashboards, mentoring junior analysts, and embedding best practices so that high-quality analytics becomes the norm, not the exception.

The role operates in a hyper-collaborative way and follows these guiding principles:

• Put customers first
• Value common sense and simplicity over complexity
• Make decisions quickly and move nimbly
• Celebrate diverse ideas
• Succeed and fail together as a team

Key Responsibilities

1.
Business & Process Analytics

• Work with stakeholders across functions to identify opportunities where data and dashboards can improve process performance and decision-making.
• Analyze AS-IS processes, identify gaps and challenges, and co-define TO-BE processes with business owners.
• Translate business problems into clear analytical questions, metrics, and reporting requirements.
• Prioritize and manage a portfolio of analytics and process improvement initiatives, tracking success measures and benefits.

2.
Data Storytelling & Visualisation

• Design and develop operational dashboards and reports (e.g. for maintenance, supply chain, manufacturing, plantation) that clearly communicate KPIs, trends, and exceptions.
• Use strong data storytelling techniques: frame insights in business language, highlight implications, and recommend actions.
• Present findings and dashboards to business users at different levels, from front-line operations to management.
• Ensure visualisations (charts, graphs, tables) are intuitive, consistent, and aligned to business needs.

3.
Data Preparation & Modelling

• Extract, clean, transform, and join data from multiple systems to build reliable analytics datasets.
• Design and improve data models (e.g. star schemas, subject-area models) that support dashboards and recurring analysis.
• Define and calculate business metrics (e.g. productivity, quality, throughput, cycle times) in a consistent and auditable way.
• Validate data quality and work with IT/data teams to resolve data issues at source where possible.

4.
Implementation & Continuous Improvement

• Collaborate with IT, data engineering, and digital teams to move analytics solutions from prototype to stable usage.
• Develop processes and tools to monitor dashboard reliability, data freshness, and metric accuracy.
• Gather feedback from users and iteratively improve dashboards, reports, and metrics.
• Identify and manage process risks and issues that impact the successful use of analytics and dashboards.

5. Mentoring, Standards & Floor-Raising

• Mentor and coach junior analysts / data team members on problem framing, SQL, data modelling, and visualisation best practices.
• Review and challenge analyses and dashboards produced by the team to ensure clarity, correctness, and consistency.
• Define and promote standards, templates, and guidelines for metrics definitions, dashboards, and documentation.
• Run or support training sessions, clinics, and "show-and-tell" sessions to raise analytics literacy among business users and analysts.
• Act as a go-to person for good analytics practice, fostering a culture of evidence-based decision-making.

Skills & Qualifications

Core

• Formal training in Computer Science,
  Mathematics or Statistics
  , providing a strong foundation in quantitative reasoning and data analysis.
• At least 6-8 years of experience in data analytics, business intelligence, or related roles.
• Strong ability to analyze data and processes, draw conclusions, and communicate findings clearly to business users, especially in structuring insights into clear narratives with the use of use of charts, graphs, and tables.
• Experience in designing and building dashboards and reports for operations (e.g. supply chain, manufacturing, maintenance, or plantation).
• Proficiency in:
• SQL and relational databases (e.g. PostgreSQL)
• Python for data manipulation and analysis
• Data visualisation tools/libraries (e.g. Matplotlib, Plotly, Seaborn, or similar)
• Demonstrated experience in coaching or guiding junior team members and in setting or enforcing analytics standards.

Strong Advantages

• Experience in deploying analytics solutions (dashboards, reports, models) into production environments and supporting their ongoing use.
• Working experience in maintenance, supply chain, manufacturing, or plantation domains.
• Exposure to process manufacturing technology & systems in agriculture, pulp and paper, palm oil refining, is a bonus.
• Formal training in
  Mathematics or Statistics
  , providing a strong foundation in quantitative reasoning and data analysis.
• Familiarity with AWS data and analytics services
  (e.g. SageMaker) and
  Databricks
  for managing, analysing, and operationalising data is an advantage.

Good-to-Have

• Experience with advanced statistical methods (e.g. regression, Bayesian inference, mixed/hierarchical models).
• Exposure to machine learning tools where they support specific analytics use cases.
• Experience with tools such as SAS, SPSS, or other analytics platforms.