Solid foundational quantitative knowledge and skills. Extensive training in math, statistics, physical science, engineering, or other related fields is required.
Experience in leading large-scale data science projects and delivering from end to end.
Deep technical expertise in machine learning and statistical modeling.
Strong knowledge of tree-based classification and regression techniques such as boosting and random forest. Expert knowledge of ML Python frameworks, packages, and libraries (e.g., pandas, numpy, scikit-learn, pytorch).
Strong hands-on programming experience with demonstrated expertise in Python.
Strong expertise in SQL programming.
Experience with data platforms such as Spark, Hadoop, BigQuery, Snowflake or others.
Experience with GCP, AWS, or other cloud computing services.
Strong communication and interpersonal influencing skills.
Excellent problem-solving and critical-thinking capabilities.