Job Description

Location:

Mumbai

Job Type:

Full-time

Experience: 0-6months

About the

We are seeking a highly motivated Associate Data Scientist

with a strong passion for energy, technology, and data-driven decision-making. In this role, you will be responsible for developing and refining

energy load forecasting models

, analyzing

customer demand patterns

, and improving

forecasting accuracy

using advanced

time series analysis

and

machine learning techniques

. Your insights will directly support

risk management, operational planning, and strategic decision-making

across the company.

If you thrive in a fast-paced, dynamic environment and enjoy solving complex

data science challenges

, we'd love to hear from you!

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. Automate

data pipelines

and

forecasting workflows

to streamline processes across departments. Monitor day-over-day

forecast variations

and communicate key insights to stakeholders. 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 like

ARIMA, Prophet, LSTMs, and regression-based models

.

Qualifications & Skills:

0-6months of 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 working 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 independently and collaboratively in a

fast-paced, dynamic environment

. Strong attention to detail, time management, and organizational skills.

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

.