Responsibilities:
− Apply advanced statistical and data analysis techniques to conduct comprehensive environmental impact assessments related to automotive vehicle lifecycles.
− Utilize source apportionment methodologies and data modeling to identify, quantify, and attribute contributions of automotive sources to air, water, and soil pollution.
− Process, analyze, and interpret large datasets from the Weather Research and Forecasting (WRF) model and other relevant atmospheric/environmental models to assess the dispersion and impact of automotive emissions.
− Analyze environmental and climate datasets to understand the impact of weather patterns and climate change on automotive-related environmental issues.
− Prepare detailed technical reports, data visualizations, dashboards, and presentations communicating complex environmental findings to internal stakeholders, regulatory bodies, and the scientific community.
− Design, manage, and analyze data from environmental monitoring programs.
− Provide expert guidance and mentorship on environmental data analysis techniques to subordinates and colleagues
Essential:
− Minimum of 4 years of professional experience in environmental data analysis, modeling, consulting, research within the automotive sector
− Strong experience applying source apportionment techniques (e.g., receptor modeling, chemical mass balance) and interpreting the results
− Good understanding of air quality modelling, emission inventories, statistical analysis, and pollution control technologies relevant to vehicles
− Proficiency in data analysis programming languages (e.g., Python, R) and relevant libraries/tools (e.g., Pandas, NumPy, SciPy, WRF).
− Experience with database management and data visualization tools (e.g. Power Bl).
− Excellent analytical, problem-solving, and quantitative skills.
− Strong written and verbal communication skills, with the ability to present complex data findings clearly.
Desirable:
− Experience with Life Cycle Assessment (LCA) methodologies and software.
− Advanced proficiency in Geographic Information Systems (GIS) for spatial environmental analysis.
− Experience working directly within the automotive industry or Tier 1 suppliers, particularly in a data-focused role.
− Knowledge of machine learning techniques applied to environmental data.
− Experience developing predictive environmental models.
− Project management experience, especially in data-intensive projects.