Chief Architect of Energy Intelligence Systems

1 week ago

Nellore, Andhra Pradesh, India beBeeDataScientist Full time ₹ 40,00,000 - ₹ 50,00,000
Senior Data Scientist - Time Series Forecasting and M&V

The Position:

We are seeking a highly skilled Senior Data Scientist to lead the development and optimization of our Forecasting and Measurement & Verification (M&V) platform. This platform supports forecasting and M&V for approximately 3 million distributed energy devices at once.

As a senior member of the Forecasting team, you will collaborate closely with data engineers, software engineers, and SREs to enhance core algorithms, improve scalability, and deliver new services. You will play a key role in translating business needs into robust data science solutions that run in production at scale. This position is ideal for someone who is passionate about using data science to drive real-world impact in the energy sector.

Key Responsibilities:

  • Design, implement, and improve scalable forecasting and M&V algorithms using statistical, machine learning, and scientific methods.
  • Apply rigorous scientific techniques such as hypothesis testing, causal inference, and uncertainty quantification to support decision-making and model evaluation.
  • Enhance the scalability and efficiency of forecasting and M&V pipelines through code and system improvements.
  • Cross-functional collaboration: work closely with engineers and SREs to resolve production issues, gather requirements, and support client needs.
  • Mentorship and knowledge sharing: mentor junior team members and educate the team on core scientific concepts in forecasting and M&V.
  • Agile participation: engage in sprint planning, estimation, reviews, and retrospectives within a Scrum framework.

Required Skills:

  • 4+ years of experience in a data science role with a focus on time-series forecasting and/or M&V.
  • Strong programming skills in Python, with experience navigating and contributing to production codebases.
  • Deep understanding of statistical and machine learning methods for time-series forecasting (e.g., ARIMA, regression models, deep learning).
  • Experience with the Python data science stack (NumPy, SciPy, Pandas, scikit-learn).
  • Hands-on experience with large-scale data processing using PySpark or Dask.
  • Strong SQL skills; experience with both RDS and NoSQL databases is advantageous.
  • Excellent communication skills with the ability to explain complex technical concepts to both technical and non-technical audiences.

Qualifications:

  • Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

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