Sustainability Analyst

1 week ago


Hyderabad, Telangana, India Dygnify Full time
Hyderabad (Hybrid) Company: Dygnify (A Funded Climate-Tech Company)

Compensation: Competitive, commensurate with skills, experience, and attitude

Dygnify is a climate-tech analytics company at the intersection of sustainability, deep technology, and financial services.

We are hiring a full-time, hybrid position for a Climate Data Modelling Analyst at Hyderabad . This is an exciting opportunity to gain hands-on experience in developing industry-ready climate models while working alongside seasoned experts and company founders.

Work with Python (including geospatial, computing, AI/ML and data analysis libraries) to automate workflows and analyse extreme climate event risk.

Leverage AI/ML toolkits and pipelines to integrate high-resolution datasets into extreme climate event models to generate actionable insights.

Strong interest in extreme climate event modelling , with prior experience developing extreme climate event models using the mentioned tools.

Proficiency in Python (including NumPy, scikit-learn, PyTorch, TensorFlow, Pandas, GeoPandas, Rasterio, GDAL, etc.).

A BTech/MTech/PhD in Civil Engineering/Geospatial/Hydrology/Water Resources, or a related field , with experience in building extreme climate event models and proficiency in extreme climate event modelling toolkits (HEC-HMS, HEC-RAS, ArcGIS/QGIS, Python, scikit-learn, GEE).

# An extreme climate event modelling practitioner who has practical experience using HEC-HMS, HEC-RAS, ArcGIS/QGIS, and Python and is well-versed in climate event risk assessment and analysis.

Hands-on experience in developing practical, industry-ready climate risk models .

Work at the forefront of climate risk intelligence & AI/ML in a fast-growing startup.

Send your CV and a short statement on your previous climate data modelling experience to with the subject line "Climate Data Modelling Analyst (Full-time) – Hyderabad" .

~ We look forward to working with talented individuals who are eager to make an impact in climate risk modelling

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