Data Scientist

at  Wenco a Hitachi Construction Machinery subsidiary

GENERAL SUMMARY: Reporting to the Manager, Global Customer Experience, the Data Scientist in the Consite Mine PoV team will be a subject matter expert (SME) in extracting valuable insights using advanced statistical models and machine learning techniques on Big Data in Cloud Computing Environments. The Data Scientist works closely with business stakeholders to understand their goals and determines how the available data can be used to achieve those goals. In collaboration with technology development teams and 3rd party ecosystem partners, they design data pipeline requirements and data modeling processes, create algorithms and predictive models to extract the information the business needs. Help analyze the data and share meaningful insights with peers. Will gain understanding of target data sets and how it should be pre-processed for reliable and scalable analytic output. Seek comprehensive definition on user requirements to ensure Analytics product meets expectations efficiently as possible, avoiding multiple iterations and/or re-work.

Responsibilities

• Data Pipeline and Data Warehouse design, in collaboration with technology development partners
• Data Collection and Preparation: Collaborate with cross-functional teams to gather data from various sources. Develop data pipelines and ensure data quality and consistency for analysis.
• Data Analysis and Modeling: Utilize advanced statistical and machine learning techniques to analyze large datasets and identify patterns, trends, and anomalies. Build predictive models to optimize mining operations and equipment maintenance.
• Visualization and Reporting: Create data visualizations and reports to communicate findings and insights to stakeholders, including management and operational teams. Develop interactive dashboards for real-time monitoring of mining processes.
• Optimization and Efficiency: Work closely with engineers and mining experts to identify areas for improvement in operational efficiency of equipment. Collaborate on the development of algorithms and tools for automated decision support.

Key Deliverables

• Data models and pipelines: These are data pipeline structures and machine learning or statistical models that are trained on data to produce outputs, such as predictions, classifications, or recommendations. They may also include the data preprocessing and postprocessing steps, such as data cleaning, feature engineering, validation, and deployment.
• Experiment designs and results: These are plans and outcomes of scientific studies that test the effects of different variables or interventions on a target metric or outcome. They may include experimental setups, data collection methods, statistical tests, and conclusions.
• Dashboards and applications: These are interactive tools that allow users to access, explore, and manipulate the data and the model outputs. They may include graphs, charts, tables, filters, sliders, and other widgets that enable data visualization and user interaction.
• Analysis reports: These are documents or presentations that summarize the findings and insights from data exploration and analysis. They may include descriptive statistics, visualizations, hypotheses, and recommendations for further actions or improvements.

Experience and Knowledge

• Strong background in programming, statistics, machine learning, data visualization, communication and problem-solving
• Experience in designing and implementing high performance data pipelines, warehouse and analytics systems on common cloud platforms (e.g. AWS)
• Proficient with data mining, cleaning, visualization, and programming languages such as Python, R, SQL, Java, JavaScript.
• Experience in using data on cloud computing, and big data platforms
• Proficient with DataOps practices, including continuous integration/continuous deployment (CI/CD) for data pipelines, and automated testing and monitoring of data workflows.
• Skills in data pipeline automation tools such as DBT, Infrastructure as code (IaC) tools like Terraform or CloudFormation for managing data infrastructure.
• Knowledge in data quality management practices, ensuring compliance and accuracy in data handling.
• Experience with orchestration and workflow management tools (e.g., Apache Airflow, Prefect) for managing and scheduling complex data workflows.
• Able to define the problem, scope the project, plan the resources, monitor the progress, evaluate the results, and deliver the solutions.
• Fluency in English is a must, Japanese is a plus.

Skills and Abilities

• Proactive technical problem solver, with strong ability to map technologies to business priorities.
• Understand business context, objectives, and impact of their work, and adhere to the ethical principles and standards of data science.
• Communicate clearly and effectively with different audiences, such as technical peers, business analysts, managers, and customers.
• Proven self-starter with ability to turn objectives into actions and results.
• Present their findings and recommendations using compelling and engaging visualizations and stories.
• Think critically and creatively and generate new ideas and insights from data.
• Contribute to a team environment towards common goals, be open to work sometimes outside of comfort zone to meet objectives.

• Data Pipeline and Data Warehouse design, in collaboration with technology development partners
• Data Collection and Preparation: Collaborate with cross-functional teams to gather data from various sources. Develop data pipelines and ensure data quality and consistency for analysis.
• Data Analysis and Modeling: Utilize advanced statistical and machine learning techniques to analyze large datasets and identify patterns, trends, and anomalies. Build predictive models to optimize mining operations and equipment maintenance.
• Visualization and Reporting: Create data visualizations and reports to communicate findings and insights to stakeholders, including management and operational teams. Develop interactive dashboards for real-time monitoring of mining processes.
• Optimization and Efficiency: Work closely with engineers and mining experts to identify areas for improvement in operational efficiency of equipment. Collaborate on the development of algorithms and tools for automated decision support