Description

Programming Libraries Expert-level proficiency in Python and its core data science libraries (Pandas| NumPy| Scikit-learn).

Strong proficiency in SQL for complex data extraction and manipulation.

Machine Learning Frameworks Hands-on experience with modern deep learning frameworks such as TensorFlow or PyTorch.

Statistical Modeling Deep understanding of statistical concepts and a wide range of machine learning algorithms| with proven experience in time-series forecasting and anomaly detection.

Big Data Technologies Demonstrable experience working with large datasets using distributed computing frameworks| specifically Apache Spark.

Database Systems Experience querying and working with data from multiple relational database systems (e.g.| PostgreSQL| Oracle| MS SQL Server).

Cloud Platforms Experience building and deploying data science solutions on a major cloud platform (AWS| GCP| or Azure).

Familiarity with their native ML services (e.g.| AWS SageMaker| Google Vertex AI) is a strong plus.

MLOps Tooling Practical experience with MLOps principles and tools for model versioning| tracking| and deployment (e.g.| MLflow| Docker)

Key Skills

Education

Bachelor's degree