Data Scientist

Key Responsibilities

1. Modeling, Analysis, & Experimentation

• Design, develop, and implement various   Machine Learning (ML) models   (e.g., predictive, prescriptive, clustering) to solve complex business problems across areas like demand forecasting, customer segmentation, or risk analysis.
  
• Conduct complex   Exploratory Data Analysis (EDA)   using statistical methodologies and advanced visualization techniques to uncover insights and define metrics.
  
• Develop and manage rigorous A/B testing frameworks and experiments to validate hypotheses and measure the impact of deployed models.
  
• Translate ambiguous business questions and large datasets into clear, actionable data science roadmaps and deliverables.
  

2. Google Cloud Platform (GCP) Focus  

• Design and implement end - to - end   ML Pipelines   using   Google Cloud Vertex AI (e.g., Feature Store, Workbench, Experiments, Pipelines).
  
• Leverage   BigQuery for massive-scale data storage, manipulation, and high-performance querying using SQL.
  
• Utilize core GCP data services such as Cloud Storage, Cloud Dataflow (Apache Beam), and Cloud Composer (Apache Airflow)   for reliable data ingestion, transformation, and workflow orchestration.
  
• Ensure solutions are scalable, cost-efficient, and maintain high standards of security and governance within the GCP environment.
  

Required Qualifications

• Experience:  5-7 years of professional experience as a Data Scientist or Machine Learning Engineer.
  
• Technical Stack:  Expert proficiency in   Python and its scientific libraries (e.g., Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch).
  
• GCP Expertise:  Strong hands-on experience with key Google Cloud services, including   BigQuery, Vertex AI, and Cloud Storage .
  
• Modeling:  Proven ability to build, train, and evaluate production-grade ML models.
  
• SQL & Data Wrangling:  Expert level SQL proficiency for large-scale data manipulation and querying.