- Ability to interpret data findings within a business context and contribute to data studies including profiling, quality assessment and exploratory analysis

- Familiarity with data governance concepts and structured analytical workflows

Classical Machine Learning :

- Hands-on experience with core supervised and unsupervised learning techniques including regression, classification, clustering, and dimensionality reduction

- Proficiency in Python or R for model development, feature engineering and evaluation

- Working knowledge of libraries such as scikit-learn and statsmodels

- Understanding of model validation techniques, performance metrics, and basic statistical inference

Distributed Data Processing :

- Practical exposure to Apache Spark using PySpark or SparkR

- Experience querying and processing large datasets in distributed environments such as Databricks, AWS EMR, or equivalent platforms

- Familiarity with SQL at scale including Spark SQL or Hive

Preferred Qualifications :

- Bachelor's degree in Statistics, Computer Science, Mathematics, or a related quantitative discipline

- Exposure to version control (Git) and collaborative development practices

- Awareness of data lakehouse architectures and partitioned data strategies