Key Responsibilities:

1. Environmental Monitoring & Lab Integration

• Design and manage data collection protocols using environmental sensors (e.g., for air quality, water pollution, soil composition).

• Collaborate with lab technicians to ensure accurate calibration and maintenance of monitoring equipment.

• Develop automated workflows to collect, clean, and store environmental data in real time.

2. AI & Machine Learning Application

• Build and apply AI/ML models to detect patterns, anomalies, or trends in environmental datasets.

• Use predictive modeling to anticipate environmental hazards such as pollution spikes, chemical leaks, or ecosystem degradation.

• Integrate AI models with real-time monitoring platforms for automated reporting and alerts.

3. Data Science & Visualization

• Conduct in-depth data analysis using Python, R, or MATLAB.

• Create interactive dashboards and visual reports using tools like Power BI, Tableau, or custom-built web apps.

• Collaborate with researchers, policy analysts, or engineers to translate data insights into actionable recommendations.

4. Cybersecurity & Data Protection

• Implement cybersecurity best practices to ensure the integrity, confidentiality, and availability of environmental data.

• Manage secure transmission protocols between sensor networks, cloud databases, and analysis platforms.

• Conduct risk assessments and establish security controls for IoT-connected environmental systems.

Required Qualifications:

• Bachelor’s or Master’s degree in Environmental Science, Data Science, Computer Science, Engineering, or a related field.

• Demonstrated experience with AI/ML tools and libraries (e.g., TensorFlow, scikit-learn, PyTorch).

• Knowledge of environmental monitoring systems and data types (e.g., EPA standards, IoT-based sampling).

• Strong programming and data handling skills (Python, SQL, R, or similar).

• Familiarity with cybersecurity practices and tools (e.g., secure API usage, encryption, basic network security).

Preferred Qualifications:

• Experience with cloud platforms like AWS, Google Cloud, or Azure for AI model deployment and data storage.

• Knowledge of geospatial tools (e.g., QGIS, ArcGIS, Google Earth Engine).

• Certifications or coursework in AI ethics, environmental regulations, or cybersecurity frameworks (e.g., NIST).

Key Soft Skills:

• Strong problem-solving ability and systems thinking mindset.

• Excellent written and verbal communication skills for interdisciplinary collaboration.

• Ability to manage multiple projects in a fast-paced, evolving tech-environment.

Work Environment:

• Hybrid role (field data acquisition + remote/cloud-based model development).

• Occasional travel to environmental sites or partner labs for on-site system integration.

• Team-oriented and often cross-disciplinary, involving coordination with ecologists, AI engineers, and policy experts.