Data Scientist

Job Purpose

Komatsu Mining Technology Solutions’ Applied Sciences Team designs and develops real-time optimization engines for automated and manned systems across the mining value stream with special focus on load & haul operation.

The Applied Sciences department is looking for someone well versed in the data science process. Someone who is proactive, data-oriented, creative, and shows good skills in computer programming and data analysis, visualization, and presentation.

This role is instrumental in developing advanced predictive analytics tools that provide actionable insights using historical and real-time data. This role requires a creative problem-solver with a strong grasp of data science methodologies, machine learning, and statistics. The selected candidate will be accountable for enabling optimization development teams, consulting teams, and customers by providing analytical tools to predict the behavior of assets.

Job Duties and Responsibilities

• Thought Leadership & Innovation: Identify areas where predictive models can be improved, propose novel approaches, and drive strategic improvements in analytics capabilities.
• Advanced Data Analysis: Investigate complex problems using structured problem-solving techniques and develop data-driven solutions.
• Predictive Modeling: Design, develop, and validate machine learning and statistical models to optimize performance across mining operations.
• Collaboration & Knowledge Sharing: Work cross-functionally to integrate predictive analytics into real-time applications and share best practices in model development and deployment.
• Technical Implementation: Write scalable, high-quality code, implement machine learning algorithms, and ensure seamless integration into operational workflows.
• Presentation & Stakeholder Engagement: Effectively communicate findings, model performance, and recommendations to both technical and non-technical stakeholders.
• Continuous Learning: Stay updated with the latest advancements in Data Science, Artificial Intelligence, and Big Data technologies to drive innovation.

Required Skills

• Proficient in at least one major programming language (Python, Scala, Java, C#, C/C++, Kotlin).Deep understanding of data structures, algorithm design, and computational complexity.
• Understanding of database theory and experience in at least one relational DBMS like SQL Server, PostgreSQL, and MySQL
• Expertise in descriptive and inferential statistics, hypothesis testing, and statistical significance.
• Strong background in probability distributions, sampling techniques, and confidence intervals.
• Experience in statistical modeling, regression analysis, and variance analysis.
• Exploratory data analysis (EDA) and data cleaning techniques.
• Expertise in data manipulation, including indexing, slicing, transposing, and reshaping data, performing operations with data frames, and extracting data from various sources such as CSV, JSON files, SQL and NoSQL databases, and web APIs.
• Strong knowledge of various machine learning techniques, including supervised, unsupervised, and reinforcement learning
• Proficiency in model selection, feature engineering, dimensionality reduction, and hyperparameter tuning.
• Experience in time series forecasting, anomaly detection, and ensemble methods.

• Understanding of artificial neural networks, deep learning concepts, and AI-driven analytic
• Experience working with distributed computing frameworks (e.g., Spark, Databricks).
• Familiarity with cloud-based analytics platforms, preferably Microsoft Azure Analytics.

Desired Skills

• Understanding of fleet optimization, route optimization, vehicle scheduling, and operational efficiency modeling.
• Familiarity with transportation simulation tools and AI-driven logistics planning.
• Familiarity with telematics, IoT data sources, and sensor-driven analytics in Load & Haul operations.
• Expertise in real-time analytics and event-driven architectures (Kafka, RabbitMQ, etc.).
• Familiarity with containerization and orchestration tools such as Docker and Kubernetes.
• Strong understanding of agile methodologies and collaborative development practices.