About the Position
A Data Scientist uses techniques that integrate traditional and non-traditional datasets to develop analytical solutions and deliver business insights for the management, planning and optimization of University of Phoenix operations, strategies and student learning. The individual applies statistical methods, machine learning, and optimization techniques to produce solutions and insights that enable customer-facing and strategical decision-making applications.
What You’ll Do
1. Participate in the execution of complex statistical and machine learning analyses on large scale data sets to understand trends, discover relationships among variables and formulate predictive insights, conduct qualitative and quantitative analyses and build analytical models on data from existing databases, observations, and business and learning processes.
2. Resolve problems in business planning, management and optimization projects that are consistent with the University’s mission and business agenda/needs by applying analytical, statistical, machine learning, simulation, and/or experimentation methods.
3. Develop data models and perform statistical and machine learning analysis by developing and maintaining efficient SQL/Python/R code.
4. Deliver results generated from existing data science driven solutions that can bring business value and interpret project results.
MINIMUM KNOWLEDGE, SKILLS, AND ABILITIES):
• Master’s degree in a quantitative field such as statistics, computer science, mathematics, economics, or finance
• One (1) year of hands-on experience in the areas of inferential statistics, machine learning, simulation and predictive modeling, including proficiency with SQL, Excel and SAS, R or Python and knowledge of Microsoft SQL Server, statistical modeling and machine learning software (such as SAS Enterprise Guide, SAS Enterprise Miner, R Studio and Jupyter Notebook)
PREFERRED KNOWLEDGE, SKILLS AND ABILITIES :
• Mathematical/statistical skills including regression analysis (linear, logistic, parametric), forecasting models, factor/component analysis, decision trees, segmentation and cluster analysis
• Knowledge of distributed data systems such as Hadoop and related technologies (Spark, Presto, Pig, Hive, etc.)
• Strong critical thinking and logical reasoning skills