Data Scientist

9 Months ago • 3-5 Years • Data Analysis

Job Summary

Job Description

SparkCognition seeks a Data Scientist with 3-5 years of experience. You'll build an AI-enabled SaaS platform for renewable energy, leveraging machine learning algorithms, deep learning frameworks, and programming skills in Python and R. Strong written and verbal communication skills are essential.
Must have:
  • Machine Learning
  • Deep Learning
  • Python & R
  • Data Science
Good to have:
  • Renewable Energy
  • Time Series
  • Cloud-native Tools
  • Workflow Orchestration
Perks:
  • Equal Opportunity
  • Reasonable Accommodations

Job Details

About the job

Who are we and why this opportunity?

SparkCognition, Inc. delivers world-class AI solutions that allow a business to solve their most critical problems, empowering them to run a more sustainable, safer, and profitable business. Our award-winning AI solutions predict future outcomes, optimize processes, and prevent cyberattacks. We partner with the world’s industry leaders to analyze, optimize, and learn from data. We augment human intelligence, drive profitable growth, and achieve operational excellence.

Drive change and create a footprint. Learn more at: SparkCognition

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


SparkCognition is an equal opportunity employer, dedicated to diversity, equality, and inclusion, and provides equal employment opportunities to all employees and applicants for employment.

SparkCognition prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.

SparkCognition is committed to providing reasonable accommodations throughout the recruiting process. If you need a reasonable accommodation, please contact us to discuss how we can assist you.

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About The Company

Bengaluru, Karnataka, India (On-Site)

Bengaluru, Karnataka, India (On-Site)

Bengaluru, Karnataka, India (On-Site)

Bengaluru, Karnataka, India (On-Site)

Bengaluru, Karnataka, India (On-Site)

Bengaluru, Karnataka, India (On-Site)

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