Experience: 5-7 years of professional experience as a Data Scientist or Machine Learning Engineer.
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.
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.
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.
