Data Scientist

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

Summary of Duties: Communicate research and findings to audiences with varying degrees of technical knowledge. Share insights about large amounts of data and/or complex relationships, to translating data analysis into coherent reports and presentations. Serve as subject matter expert in the response to RFI/RFPs including defining solutions, technical writing, demonstrations, and client presentations. Serve as a trusted advisor to all stakeholders in defining and implementing ethical AI best practices. Research and develop methods to detect and quantify bias, understand where biases come from, explain models and their decisions, and attribute responsibility. Assist with translating concepts and research outcomes into product requirements. Evangelize and educate with respect to data ethics and responsible AI. Build analytic models, to support business leaders with data driven insights that address immediate business problems and objectives. Translate analytic insights into coherent reports and presentations for consumers with varying degrees of technical knowledge. Advocate for change management – beyond innovating new ideas, you will help in the adoption of new processes.

May work remotely pursuant to SAS’ Flexible Work Program.

Qualifications: Position requires a Bachelor’s degree in Computer Science, Statistics, or a related quantitative field plus three (3) years of experience in the job offered or three (3) years of experience in a related occupation. Employer will alternatively accept a Master’s degree in Computer Science, Statistics, or a related quantitative field plus one (1) year of experience in the job offered or in a related occupation. Experience may be gained concurrent to attainment of graduate degree.

Position requires knowledge of advanced statistical concepts, techniques, and appropriate use for techniques such as regression, properties of distribution and statistical tests. Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.