About the role

Responsibilities:

Build and deploy machine learning models for forecasting, anomaly detection, predictive maintenance, optimization, and business intelligence use cases.
Analyze large structured and unstructured datasets to identify trends, business opportunities, and operational inefficiencies.
Design and execute experiments, A/B tests, and statistical analyses to support strategic decision-making.
Collaborate with business, product, engineering, and operations teams to translate business problems into scalable data science solutions.
Develop forecasting models for network usage, energy consumption, infrastructure utilization, and operational KPIs.
Create automated dashboards, monitoring systems, and scalable data pipelines.
Improve model performance through feature engineering, hyperparameter tuning, and continuous evaluation.
Present insights and recommendations to leadership and cross-functional stakeholders.
Mentor junior analysts/data scientists and contribute to data science best practices.

Requirements:

Bachelor's or master's degree in computer science, data science, statistics, mathematics, engineering, or related quantitative disciplines.
Strong proficiency in Python and SQL.
Hands-on experience with Scikit-learn, XGBoost, TensorFlow, or PyTorch.
Strong understanding of statistics, probability, hypothesis testing, and predictive modeling.
Experience with Spark, Hadoop, or distributed computing frameworks.
Expertise in Power BI, Tableau, or Looker.
Familiarity with AWS, GCP, or Azure is preferred.
Knowledge of MLOps, Docker, Airflow, or deployment pipelines is a plus.

Functional Skills:

Preferred Experience:

3-5 years of experience in data science, machine learning, or advanced analytics.
Experience in forecasting, optimization, anomaly detection, or predictive analytics.
Exposure to telecom/network analytics, IoT analytics, or infrastructure operations is an added advantage.

Data Scientist II