Job Description & Summary
At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth.

Responsibilities:
Location: Hyderabad
Experience: 4-7 years of experience

We are seeking a skilled Data Scientist / Machine Learning Engineer to drive data-driven solutions and innovation. In this role, you will perform exploratory data analysis (EDA), apply statistical testing, and develop advanced machine learning models to solve complex business problems. You will design and maintain robust data pipelines and ETL processes to integrate data from diverse sources into Snowflake or Data Lake environments. Leveraging cutting-edge statistical and machine learning techniques, you will extract valuable insights and lead AI research initiatives to explore novel analytical approaches.
Key Responsibilities:




· Conduct exploratory data analysis using visualizations to understand data patterns and structures.
· Perform statistical analysis and hypothesis testing to validate assumptions.
· Develop, deploy, evaluate, and fine-tune machine learning models for business challenges.
· Design and implement scalable data pipelines, ETL workflows, and data integration strategies into Snowflake/Data Lake platforms.
· Utilize advanced statistical and machine learning methods to derive actionable insights.
· Lead AI research projects focused on innovative data analysis techniques.
Qualifications:
· 4+ years of hands-on experience building data analytics, data engineering, and machine learning solutions.
· Robust expertise with Azure Stack services and Snowflake.
· Proficient in SQL with deep knowledge of dimensional modeling.
· Experience creating interactive dashboards using Power BI or Tableau.
· Familiarity with advanced statistical analysis, machine learni

📌 Data Engineer (Hyderabad)
🏢 PwC
📍 Hyderabad