Name of employer
December 17, 2019
  • Diverse & challenging projects with our PACE Analytics team
  • Apply your creativity to our huge mine datasets
  • Utilise your hands-on coding skills in R and/ or Python
  • Work in a flexible work environment where we prioritise a healthy work / life balance

About the role

We are looking for a Principal Data Scientist to work with challenging and exciting analytics problems and make a positive impact to the business, while engaging with a global team of technology experts and engineers. This will involve gaining substantial experience working with our data systems to develop state-of-the-art data science solutions for our business’s data problems.

Reporting to the Manager Data Analytics you will support the PACE data analytics team to design and continually improve services that solve business problems and provide insight to making improved decisions. You will utilise your strong hands-on coding skills in R, Python and/ or Scala to:

  • Perform exploratory analysis of data sets to develop and refine preliminary hypothesis
  • Engineer data pipelines from source systems to predictive models
  • Develop interfaces that visualise information for business users
  • Provide expertise on statistical, mathematical, and machine learning concepts and conduct research, design, implement and validate cutting-edge algorithms and predictive statistical models
  • You will also support the PACE data analytics team to:
  • Ensure best in class processes and techniques to develop and deploy data analytics solutions to the business
  • Ensure the quality assurance and quality control of all data analytics products released to the business
  • Capture and record the intellectual property generated.
  • Share what has been developed in GitHub to help foster a new generation of data driven decision makers and enable them to improve upon what has already been done
  • Engage with clients and key stakeholders to build their understanding of data analytics
  • Foster an innovative and “disruptive” technology culture to identify and drive value creating opportunities
  • Help the broader PACE team and client prioritize by advising technical complexity of identified opportunities
  • Provide training and knowledge transfer to support the deployment and continuity of the analytics products developed

About you

You will ideally have a PHD in Mathematics, Statistics, Computer Science, Electrical Engineering or another quantitative field, combined with several years of experience in data science projects, preferably using agile methodologies.
You should also have experience with some or all of the below:

  • Implementation of data science projects, including familiarity with machine learning libraries, and frameworks (e.g. Sci-kit Learn, H2O, TensorFlow)
  • Proven analytical skills
  • An ability to translate data into meaningful questions and insights
  • Experience using the following software/tools
  • Experience querying databases and using statistical computer languages and packages: Python (Sci-kit Learn, numpy, pandas, Tensorflow, Keras), R, Matlab, SQL
  • Experience creating and using advanced machine learning algorithms and statistics: regression, classification, simulation, scenario analysis, modeling, clustering, decision tree, neural networks, NLP, etc.
  • Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Network Analysis, etc.
  • Experience visualizing/presenting data for stakeholders using: Matplotlib, ggplot, Seaborn, Bokeh, Plotly, D3, Tableau , etc.
  • Experience using web services: S3, Redshift, etc.
  • Experience with distributed data/ computing tools: Map/Reduce, Hadoop, Hive, Spark (desirable)
  • Software engineering experience (desirable)
  • Cloud experience (e.g. AWS and/or Azure) (desirable)
  • Ideally Bilingual, French and English

Where you will be working

The PACE Analytics team is focused on improving performance through smarter ways of working by driving an innovative and “disruptive” technology culture. By partnering with Rio Tinto product groups and functions, our team is developing innovative ideas to solve problems and enable better decisions through data analytics.

Data science roles have diverse, multi-industry backgrounds and comprehensive skills in the fields of data modelling, engineering, and visualization as well as advanced analytics techniques including machine learning and optimisation. These skills are used to turn data into information that can be used to make improved decisions and generate competitive advantage for Rio Tinto.

About us

As pioneers in mining and metals, we produce materials essential to human progress.

Our long history is filled with firsts. We’ve developed some of the world’s largest and best quality mines and operations, and our people work in around 35 countries across six continents. Aluminium and copper, diamonds, gold and industrial minerals, iron ore, coal and uranium: our materials make up the world around us. You’ll find them in smartphones, planes, cars, hospitals and throughout your home.

Creating an inclusive and diverse workforce

We are a diverse team of talented, enthusiastic individuals who foster a culture of inclusion. No matter how they may differ, our people share one thing in common. It’s a belief that work is more rewarding when we are accepted and valued for our differences, not judged by them. We all have something to contribute, and it’s this contribution that makes for a great organization and fulfilling career.

We value proven analytical skills, a curious mind and a collaborative approach in our team members. If you’re ready for a new challenge and an opportunityto make a positive impact on our business performance, we want to hear from you!

Please note, in order to be successfully considered for this role you must complete all pre-screening questions.

Name of employer

Rio Tinto

Place of employment



Artificial intelligence

Years of experience

0 to 2 years

Job category


Number of available positions


Type of job

Full time


English, French


Until December 17, 2019