Position Summary:

SparkCognition’s Renewable Suite is the world's leading AI enabled SaaS platform that provides asset performance management and prescriptive analytics software to ever growing renewable energy power plants by combining the power of machine learning, big data, and deep domain expertise. Our platform works across multiple asset types such as wind, solar, storage and DC coupled assets to offer insights to our customers across their fleets. As a Data Scientist you will be working closely with team members across domains with deep technical skills and passion for clean energy.

Join us in:

• Bringing large volumes of data from wind, solar, storage sites to cloud
• Building an Asset Performance Management and Predictive Analytics platform for renewables
• Architecting scalable data ETL, MLOps pipelines, data ingestion and workflows 
• Work closely with delivery engineering team for successful hand-off of with detailed documentation of ETL and customer workflow 

You’ll have:

• Masters in Data Science, Statistics, Engineering or related fields or 3-5 years of relevant experience
• Strong understanding of machine learning algorithms & principles (regression analysis, time series, probabilistic models, supervised classification and unsupervised learning), and their application.
• Familiarity with Deep Learning frameworks such as TensorFlow and PyTorch. Experience with machine learning frameworks, such as sci-kit-learn, PyTorch, TensorFlow, and Keras
• Excellent programming skills in prototyping languages such as Python and R
• Independent thinking skills and the desire to learn new techniques/technologies
• Strong written and verbal communication skills with the ability to translate complex technical topics to internal and external stakeholders
• Most importantly, a can-do attitude and desire to make a difference is an absolute must

It would be great if you had: 

• Experience in renewable energy or IoT time series data
• Experience in workflow orchestration technologies like Airflow or Prefect
• Experience working with cloud-native tools, particularly AWS or GCP