Data Scientist, Senior

Arizona State University•Tempe, AZ

1d•$90,000 - $114,000•Onsite

About The Position

Supports ASU’s Actionable Analytics initiatives through the application of advanced data science, machine learning, statistical analysis, and artificial intelligence techniques, including the use of large language models (LLMs) and other foundation model technologies. Serves as a senior-level contributor responsible for designing, developing, validating, deploying, and monitoring analytical and AI-enabled solutions that address operational and strategic organizational needs.

Requirements

Bachelor's degree and five (5) years of experience appropriate to the area of assignment/field; OR, Any equivalent combination of experience and/or training from which comparable knowledge, skills and abilities have been achieved.
Proficiency in Python and SQL, including experience leveraging libraries, frameworks, and APIs used for machine learning, data science, and generative AI applications.
Demonstrated ability to clearly and accurately summarize findings and recommendations to technical and non-technical stakeholders to inform decision making.
Demonstrated ability to lead complex analytical projects, prioritize competing demands, and complete projects on time and within scope.
Demonstrated ability to work effectively both independently and collaboratively as part of a team.
Demonstrated ability to translate stakeholder needs into appropriate, functional, and informative analytical and AI-enabled solutions.

Nice To Haves

Demonstrated knowledge of and experience applying generative AI and large language model (LLM) technologies, using a wide-ranging suite of structured and unstructured data sources and success outcomes, as well as advanced traditional and modern/machine learning predictive modeling methodologies.
Experience conducting all phases of analytics, machine learning, and AI solution development, including data acquisition and extraction, data cleaning, data exploration, feature engineering, prompt engineering, model development, model validation/testing, deployment, monitoring, evaluation, and ongoing support.
Experience developing, validating, deploying, and maintaining machine learning models and AI-enabled applications, including solutions leveraging large language models (LLMs), foundation models, and retrieval-augmented generation (RAG) techniques in cloud or production environments.
Demonstrated knowledge of machine learning and AI best practices, including cross-validation, hyperparameter tuning, prompt optimization, model monitoring, performance evaluation, explainability, and responsible AI principles.
Experience evaluating, selecting, and applying large language models (LLMs), foundation models, and generative AI technologies to support knowledge discovery, workflow automation, decision support, and business process improvement.
Demonstrated working knowledge of higher education data systems, including specialized data mining, natural language processing, knowledge discovery, and data visualization techniques.
Experience using cloud-based analytics, machine learning, and AI services to develop, deploy, monitor, and support analytical and AI-enabled solutions.
Experience using version control systems and collaborative development tools.
Experience using data visualization tools and techniques to communicate complex analytical findings and insights.
Experience mentoring or providing technical guidance to analysts, data scientists, or project teams.

Responsibilities

Leverages machine learning, artificial intelligence, large language models (LLMs), and other advanced analytical techniques to develop innovative solutions that address operational and strategic institutional needs.
Promotes the responsible, ethical, secure, and compliant use of data science, machine learning, and artificial intelligence technologies in accordance with institutional policies and applicable regulations.
Determines and develops advanced research design methodologies, data analysis approaches, statistical modeling procedures, machine learning solutions, and generative AI applications to meet operational and strategic organizational needs.
Conducts all phases of analytics, machine learning, and AI solution development, including data acquisition and extraction, data cleaning, data exploration, feature engineering, prompt engineering, model development, model validation/testing deployment, monitoring, evaluation, and ongoing support.
Selects and applies advanced statistical, predictive modeling, machine learning, large language model (LLM), and generative AI techniques to generate actionable insights and address complex organizational challenges using structured and unstructured data sources.
Develops, deploys, integrates, and maintains statistical models, machine learning models, and AI-enabled solutions, including applications leveraging large language models (LLMs), foundation models, and retrieval-augmented generation (RAG) techniques in development and production environments.
Implements monitoring and evaluation processes for statistical models, machine learning models, and AI-enabled solutions to assess performance and enable continuous improvement.
Collaborates with technical and non-technical stakeholders to translate business needs into analytical solutions and actionable recommendations.
Creates and maintains technical documentation, analytical workflows, and model documentation.
Supports the adoption of machine learning operationalization (MLOps/AIOps) practices, including version control, model deployment, workflow automation, and reproducibility.
Learns, evaluates, and adopts new analytical methodologies, technologies, and tools to meet changing organizational needs.
Presents analytical findings and recommendations through written reports, presentations, and data visualizations to technical and non-technical stakeholders.
Provides technical guidance and mentorship to junior staff members and project teams as appropriate.