Roles – taiwoosunrinde.com

Geostatistical Modeling & Analysis

Developed and optimized a Python-based workflow to select estimator methods and modeling parameters for interpolating the Rock Quality Designation (RQD) model, ensuring alignment with reconciliation standards in the Climax mining district.
Creating Python scripts to automate resource modeling and estimation workflows, improving efficiency and repeatability.
Performing variogram analysis using PyGSLIB and RMSP (a Python-based geostatistical modeling package) to assess spatial correlation, continuity, and geological trends.
Designing variogram workflows based on data density and geological domains to improve model accuracy.
Creating workflows for multivariate stochastic simulation to support mine planning, drilling optimization, and ore routing decisions.
Performing resource grade interpolation and spatial modeling to support geological assessments.

Tool Development & Automation

Developing an internal long-range model Python package to streamline use of RMSP for geology modelers, including custom wrappers and Excel-based parameter configuration.
Building and maintaining a CI/CD pipeline in Azure DevOps for LRMPY, a private Python package for geostatistical modeling.
Automating build, test, and release processes using YAML pipelines with unit testing, linting, and packaging.
Configuring secure deployment to a private PyPI repository and managing credentials with Azure Key Vault.
Improving development efficiency and reducing manual deployment time by over 80% through continuous integration practices.

Training & Collaboration

Training exploration teams on Python and RMSP, focusing on practical applications in geologic resource modeling and workflow automation.
Collaborating with geologists and data scientists to align modeling tools with operational needs and improve resource evaluation processes.

Geostatistical Modeling & Data Science

Cleaned and prepared geological datasets using data science frameworks to support geostatistical analysis and modeling.
Performed exploratory data analysis and statistical interpretation of assay and composite data using Python to inform resource modeling and evaluation.
Created Python scripts to automate resource modeling and estimation workflows, improving efficiency and reproducibility.
Coded geological attributes (ore type, lithology, alterations) into block models and composite data to ensure accurate geological interpretation during interpolation.
Performed variogram analysis using PyGSLIB and RMSP to assess spatial correlation, continuity, and geological trends; designed variogram workflows based on data density and domain characteristics.
Conducted resource grade interpolation using Python to estimate copper distribution in the Safford and Morenci districts.
Created workflows for multivariate stochastic simulation to support mine planning, drilling optimization, and ore routing decisions.
Developed a leave-one-hole-out cross-validation and point validation workflow using Python for the El Abra Mining District.
Designed a streamlined process for statistical validation of block models, using the Chino-Cobre Mining District as a case study.

Machine Learning & Application Development

Extracted, standardized, and preprocessed datasets to ensure high-quality input for machine learning models.
Developed machine learning models (CNN, FNN) to automate mineral phase identification from X-ray diffraction (XRD) data.
Built executable applications to deploy ML models for automated mineral phase determination from XRD data.
Designed and implemented GUI tools for dynamic visualization of acid solubility and quick leach test results.

Tooling & Team Enablement

Developed an internal long-range model Python package to streamline use of RMSP, including custom wrappers and Excel-based parameter configuration.
Created a function to enhance visualization of RMSP tab results, enabling flexible layout and consolidated HTML output.
Trained exploration teams on Python and RMSP, focusing on practical applications in geologic resource modeling.

Data cleaning and preparation of data for geostatistical analysis using data science framework
Statistical and Exploratory Data Analysis of copper assay data with python
Created python scripts to automate resource modelling and estimation workflow
Performed variogram analysis to determine spatial correlation within the copper assay data using PyGSLIB and RMSP python library.
Developed and presented ideas to team lead and manager on how to implement machine learning in automating the classification of rock lithologies within the exploration and modelling workflow.
Developed approach to improve the team’s current modelling workflow

Created sample identification labels and submitting samples for laboratory analysis using computerized data transfer systems.
Logged drill cuttings and drilled core samples to identify rock types, copper mineralization, alteration, and acid consumption
Applied geostatistical algorithms and data analytic techniques to create block model for resource estimation and forecast modeling.
Quantified uncertainty within the resource estimate using variogram analysis and multivariate transform simulation with python

Setup machine learning framework/workflow for resource modeling team at Exodus dry mine, Nevada Gold Mines.
Optimized rock classification workflow by developing rock classification system using convolutional neural network algorithm. This improves time used for manual classification by 80%.
Designed and deployed the model via a web application using streamlit. Web-App Demo
Incorporated geochemical data to understand and translate complex business problems into machine learning problems.
Documented step by step procedure used in developing rock classification system in the SOP delivered to the Mineral Resource Team.
Working on object recognition using tensorflow to further identify sub-rock types within some geological core boxes.

Identified, analyzed and interpreted trends in complex data sets using supervised and unsupervised learning techniques.
Used Python to manage, analyze, and visualize large data sets.
Built machine learning model using linear regression and support vector machine with an 80% accuracy to predict customer’s reaction on the new product to be released by one of the company’s client.
Applied appropriate data science techniques to develop an insurance prediction system (95% accuracy) that predicts the best policies for individual clients.

Created and deployed an enterprise GIS system for the company using Geoserver, apache Tomcat, PostGres, and Ngrok tunneling
Used GIS analytic tools to develop an eMarketing tool which was a channel for marketing the company’s product. This increased sales by 60%
Analyzed transactions to build logical business intelligence model for real-time reporting
Developed a geolocation and suitability map to help access the performance of a client product within the states of
Carried out a GIS-Based suitability analysis for a telecommunication company using multi-criteria decision analytic tools. The suitability map produced was implemented in strategic locations by the client. This improved the quality of services delivered in the location by 80%.