Data Scientist II

Amazon; Delhi; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Amazon Inc. is seeking a data science professional to build and deploy ML models for forecasting, anomaly detection, and operational optimization.

You will analyze large datasets, design experiments, and partner with business, product, and engineering teams to translate problems into scalable data solutions. You will develop forecasting models for network usage and KPIs, create dashboards and data pipelines, improve model performance, present insights to leadership, and mentor junior analysts

Qualifications

• Strong proficiency in Python and SQL.
• Hands-on experience with Scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Experience with Spark or Hadoop.
• Expertise in Power BI, Tableau, or Looker.
• Familiarity with AWS, GCP, or Azure is preferred.
• Knowledge of MLOps and deployment pipelines is a plus.

Responsibilities

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

Skills

Python

SQL

Scikit-learn

XGBoost

TensorFlow

PyTorch

Statistics

Hypothesis testing

Problem solving

Education

Bachelor's or Master's in CS/Data Science/Statistics

Tools

Spark

Hadoop

Power BI

Tableau

Looker

AWS

GCP

Azure

Airflow

Docker

Job description

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:

• Strong analytical and problem‑solving mindset.
• Ability to independently drive projects and collaborate with stakeholders.

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.