Data Science
Python
Git
GitHubAbout the Company
Blue Sky Analytics is a Climate Tech startup that combines the power of AI & Satellite data to aid in the creation of a global environmental data stack. Our funders include Beenext and Rainmatter. Over the next 12 months, we aim to expand to 10 environmental data-sets spanning water, land, heat, and more!
We are looking for a data scientist to join its growing team. This position will require you to think and act on the geospatial architecture and data needs (specifically geospatial data) of the company. This position is strategic and will also require you to collaborate closely with data engineers, data scientists, software developers and even colleagues from other business functions. Come save the planet with us!
Your Role
Manage: It goes without saying that you will be handling large amounts of image and location datasets. You will develop dataframes and automated pipelines of data from multiple sources. You are expected to know how to visualize them and use machine learning algorithms to be able to make predictions. You will be working across teams to get the job done.
Analyze: You will curate and analyze vast amounts of geospatial datasets like satellite imagery, elevation data, meteorological datasets, openstreetmaps, demographic data, socio-econometric data and topography to extract useful insights about the events happening on our planet.
Develop: You will be required to develop processes and tools to monitor and analyze data and its accuracy. You will develop innovative algorithms which will be useful in tracking global environmental problems like depleting water levels, illegal tree logging, and even tracking of oil-spills.
Demonstrate: A familiarity with working in geospatial libraries such as GDAL/Rasterio for reading/writing of data, and use of QGIS in making visualizations. This will also extend to using advanced statistical techniques and applying concepts like regression, properties of distribution, and conduct other statistical tests.
Produce: With all the hard work being put into data creation and management, it has to be used! You will be able to produce maps showing (but not limited to) spatial distribution of various kinds of data, including emission statistics and pollution hotspots. In addition, you will produce reports that contain maps, visualizations and other resources developed over the course of managing these datasets.
Requirements
These are must have skill-sets that we are looking for:
- Excellent coding skills in Python (including deep familiarity with NumPy, SciPy, pandas).
- Significant experience with git, GitHub, SQL, AWS (S3 and EC2).
- Worked on GIS and is familiar with geospatial libraries such as GDAL and rasterio to read/write the data, a GIS software such as QGIS for visualisation and query, and basic machine learning algorithms to make predictions.
- Demonstrable experience implementing efficient neural network models and deploying them in a production environment.
- Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
- Capable of writing clear and lucid reports and demystifying data for the rest of us.
- Be curious and care about the planet!
- Minimum 2 years of demonstrable industry experience working with large and noisy datasets.
Benefits
- Work from anywhere: Work by the beach or from the mountains.
- Open source at heart: We are building a community where you can use, contribute and collaborate on.
- Own a slice of the pie: Possibility of becoming an owner by investing in ESOPs.
- Flexible timings: Fit your work around your lifestyle.
- Comprehensive health cover: Health cover for you and your dependents to keep you tension free.
- Work Machine of choice: Buy a device and own it after completing a year at BSA.
- Quarterly Retreats: Yes there's work-but then there's all the non-work+fun aspect aka the retreat!
- Yearly vacations: Take time off to rest and get ready for the next big assignment by availing the paid leaves.
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