


About 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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Role and Responsibilities
- Execute data mining projects, training and deploying models over a typical duration of 2 -12 months.
- The ideal candidate should be able to innovate, analyze the customer requirement, develop a solution in the time box of the project plan, execute and deploy the solution.
- Integrate the data mining projects embedded data mining applications in the FogHorn platform (on Docker or Android).
Core Qualifications
Candidates must meet ALL of the following qualifications:
- Have analyzed, trained and deployed at least three data mining models in the past. If the candidate did not directly deploy their own models, they will have worked with others who have put their models into production. The models should have been validated as robust over at least an initial time period.
- Three years of industry work experience, developing data mining models which were deployed and used.
- Programming experience in Python is core using data mining related libraries like Scikit-Learn. Other relevant Python mining libraries include NumPy, SciPy and Pandas.
- Data mining algorithm experience in at least 3 algorithms across: prediction (statistical regression, neural nets, deep learning, decision trees, SVM, ensembles), clustering (k-means, DBSCAN or other) or Bayesian networks
Bonus Qualifications
Any of the following extra qualifications will make a candidate more competitive:
- Soft Skills
- Sets expectations, develops project plans and meets expectations.
- Experience adapting technical dialogue to the right level for the audience (i.e. executives) or specific jargon for a given vertical market and job function.
- Technical skills
- Commonly, candidates have a MS or Ph.D. in Computer Science, Math, Statistics or an engineering technical discipline. BS candidates with experience are considered.
- Have managed past models in production over their full life cycle until model replacement is needed. Have developed automated model refreshing on newer data. Have developed frameworks for model automation as a prototype for product.
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- Energy: Oil and Gas, Wind Turbines
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About FogHorn Systems
FogHorn is a leading developer of “edge intelligence” software for industrial and commercial IoT application solutions. FogHorn’s Lightning software platform brings the power of advanced analytics and machine learning to the on-premise edge environment enabling a new class of applications for advanced monitoring and diagnostics, machine performance optimization, proactive maintenance and operational intelligence use cases. FogHorn’s technology is ideally suited for OEMs, systems integrators and end customers in manufacturing, power and water, oil and gas, renewable energy, mining, transportation, healthcare, retail, as well as Smart Grid, Smart City, Smart Building and connected vehicle applications.
Press: https://www.foghorn.io/press-room/">https://www.foghorn.io/press-room/
Awards: https://www.foghorn.io/awards-and-recognition/">https://www.foghorn.io/awards-and-recognition/
- 2019 Edge Computing Company of the Year – Compass Intelligence
- 2019 Internet of Things 50: 10 Coolest Industrial IoT Companies – CRN
- 2018 IoT Planforms Leadership Award & Edge Computing Excellence – IoT Evolution World Magazine
- 2018 10 Hot IoT Startups to Watch – Network World. (Gartner estimated 20 billion connected things in use worldwide by 2020)
- 2018 Winner in Artificial Intelligence and Machine Learning – Globe Awards
- 2018 Ten Edge Computing Vendors to Watch – ZDNet & 451 Research
- 2018 The 10 Most Innovative AI Solution Providers – Insights Success
- 2018 The AI 100 – CB Insights
- 2017 Cool Vendor in IoT Edge Computing – Gartner
- 2017 20 Most Promising AI Service Providers – CIO Review
Our Series A round was for $15 million. Our Series B round was for $30 million October 2017. Investors include: Saudi Aramco Energy Ventures, Intel Capital, GE, Dell, Bosch, Honeywell and The Hive.
About the Data Science Solutions team
In 2018, our Data Science Solutions team grew from 4 to 9. We are growing again from 11. We work on revenue generating projects for clients, such as predictive maintenance, time to failure, manufacturing defects. About half of our projects have been related to vision recognition or deep learning. We are not only working on consulting projects but developing vertical solution applications that run on our Lightning platform, with embedded data mining.
Our data scientists like our team because:
- We care about “best practices”
- Have a direct impact on the company’s revenue
- Give or receive mentoring as part of the collaborative process
- Questions and challenging the status quo with data is safe
- Intellectual curiosity balanced with humility
- Present papers or projects in our “Thought Leadership” meeting series, to support continuous learning



