Job Overview
- Design and implement algorithms to improve the accuracy, stability, and interpretability of air quality sensor data, including calibration, anomaly detection, cross-interference mitigation, and signal correction.
- Conduct in-depth research on sensor behavior and environmental interactions to inform algorithm and model development.
- Develop, implement, and optimize machine learning and statistical models for air quality forecasting, source apportionment, anomaly detection, and trend analysis.
- Collect, clean, and validate large environmental datasets from air quality sensors, meteorological stations, satellite imagery, and public databases.
- Build and maintain robust data pipelines for automated ingestion, pre-processing, anomaly handling, and quality control.
- Conduct exploratory data analysis (EDA) to uncover hidden patterns, correlations, and insights within environmental data.
- Integrate external datasets (e.g., satellite data, weather data) to enrich analyses and improve model performance.
- Evaluate and validate model and algorithm performance using appropriate metrics, lab results, and field data; iterate for continuous improvement.
- Collaborate with software and embedded systems teams to deploy algorithms and models on cloud and edge-based platforms.
- Develop interactive dashboards and visualization tools to monitor sensor behavior, communicate insights, and display model results.
- Generate clear and comprehensive reports, highlighting environmental trends, pollution sources, and actionable insights for internal teams and external stakeholders.
- Provide predictive modeling and decision support tools for use in environmental policy, health risk assessment, and operational strategies.
- Document algorithm architecture, data processes, validation procedures, and research findings for internal use and cross-team collaboration.
- Stay up to date with emerging trends in air quality monitoring, machine learning, environmental data science, and regulatory frameworks.
- Collaborate cross-functionally with environmental scientists, sensor engineers, software developers, and product managers to align on data requirements and solution design.
- Support team members by contributing to code development, assisting with debugging, and sharing data science best practices.
- Participate in research publications, conference presentations, and innovation initiatives as applicable.
- Strong knowledge of algorithm development, signal processing, and statistical analysis.
- Ability to design and deploy models in a cloud or embedded environment.
- Excellent problem-solving and communication skills.
- Passion for environmental sustainability and clean-tech.
- Proficient in Python (Pandas, NumPy, SciPy) for data analysis and scientific computing.
- Experience with R for statistical analysis and data visualization.
- Skilled in SQL for data querying and relational database management.
- Working knowledge of NoSQL databases for unstructured data handling.
- Strong foundation in inferential statistics, including hypothesis testing, ANOVA, and regression analysis.
- Expertise in time-series analysis and forecasting models. (e.g., ARIMA, Prophet)
- Familiarity with geospatial statistics and spatial data analysis.
- Understanding of model evaluation metrics: RMSE, MAE, R², precision, recall, F1-score.
- Experience with supervised learning. (linear regression, random forest, gradient boosting)
- Knowledge of unsupervised learning methods. (K-Means, DBSCAN)
- Proficient in Scikit-learn; exposure to TensorFlow or PyTorch is a plus.
- Experience with anomaly detection, source apportionment, and pollution forecasting.
- Ability to build and manage automated data pipelines. (e.g., Apache Airflow)
- Proficiency in data cleaning, preprocessing, and anomaly handling.
- Experience with large, multi-source environmental datasets.
- Familiarity with cloud platforms (AWS, GCP, Azure) for model deployment.
- Knowledge of edge computing for real-time sensor data processing.
- Version control using Git.
- Skilled in data visualization tools: Matplotlib, Seaborn, Plotly, Dash, Tableau, Power BI.
- Ability to translate complex data into actionable visual insights.
- Excellent verbal and written communication skills.
- Understanding of air pollutants (PM2.5, NOx, O3, SO2, CO) and their impacts.
- Knowledge of meteorological factors (wind, humidity, temperature) affecting air quality.
- Familiarity with sensor technologies and calibration techniques.
- Awareness of environmental regulations. (CPCB, EPA, WHO standards)
- Strong EDA (Exploratory Data Analysis) capabilities.
- Research experience in model development and algorithm optimization.
- Contributions to scientific publications or technical documentation.
- Strong problem-solving and critical thinking skills.
- High attention to detail in analysis and reporting.
- Effective collaboration with cross-functional teams.
- Self-motivated with a passion for environmental sustainability.
Location
How to Apply
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