Associate Data Scientist

Associate Data Scientist

Key Responsibilities

• Develop and enhance

  energy load forecasting models

  using

  time series forecasting

  , statistical modeling, and machine learning techniques.
• Analyze historical and real-time energy consumption data to identify trends and improve forecasting accuracy.
• Investigate discrepancies between forecasted and actual energy usage, providing actionable insights to stakeholders.
• Automate

  data pipelines

  and forecasting workflows to streamline processes across departments.
• Monitor day-over-day forecast variations and communicate key insights.
• Work closely with internal teams and external vendors to refine forecasting methodologies.
• Perform

  scenario analysis

  to assess seasonal patterns, anomalies, and market trends.
• Continuously optimize forecasting models, leveraging techniques such as

  ARIMA, Prophet, LSTMs, and regression-based models

  .

Qualifications & Skills

• Relevant experience in

  data science

  , preferably in

  energy load forecasting

  , demand prediction, or a related field.
• Strong expertise in

  time series analysis

  , forecasting algorithms, and statistical modeling.
• Proficiency in

  Python

  , with experience using libraries such as

  pandas, NumPy, scikit-learn, statsmodels, and TensorFlow/PyTorch

  .
• Experience with

  SQL

  and handling large datasets.
• Hands-on experience with forecasting models like

  ARIMA, SARIMA, Prophet, LSTMs, XGBoost, and random forests

  .
• Familiarity with

  feature engineering

  , anomaly detection, and seasonality analysis.
• Strong analytical and problem-solving skills with a data-driven mindset.
• Excellent communication skills, with the ability to translate technical findings into business insights.
• Ability to work both independently and collaboratively in a dynamic environment.

Preferred Qualifications (Nice to Have)

• Experience working with

  energy market data, smart meter analytics, or grid forecasting

  .
• Knowledge of

  cloud platforms (AWS)

  for deploying forecasting models.
• Experience with

  big data technologies

  such as

  Spark or Hadoop

  .