Data Scientist 2

This position requires in-scope poly, within 7 years.

• Programming Languages: Proficiency in programming languages such as Python and R is crucial for data manipulation, analysis, and implementing algorithms. Python is favored for its simplicity and extensive libraries (like NumPy and pandas), while R is preferred for statistical analysis and data visualization.
• Statistical Analysis: A strong foundation in statistics and probability is necessary for analyzing data accurately and making informed decisions. Understanding concepts like regression analysis, hypothesis testing, and statistical distributions is essential.
• Machine Learning: Knowledge of machine learning algorithms and frameworks (such as TensorFlow and Scikit-Learn) is vital for building predictive models and automating decision-making processes.
• Data Wrangling: The ability to clean and organize complex datasets is critical. Data wrangling involves transforming raw data into a usable format, which is often time-consuming but necessary for effective analysis.
• Database Management: Familiarity with SQL and database management systems (like PostgreSQL and MongoDB) is essential for extracting and manipulating data stored in relational databases.
• Data Visualization: Skills in data visualization tools (such as Tableau and Matplotlib) help communicate findings effectively. Creating charts, graphs, and dashboards is crucial for making data understandable to stakeholders.

Bachelor's degree from an accredited college or university in a quantitative discipline (e.g., statistics, mathematics, operations research, engineering or computer science). Five (5) years of experience analyzing datasets and developing analytics, five (5) years of experience programming with data analysis software such as R, Python, SAS, or MATLAB. An additional four (4) years of experience in software development, cloud development, analyzing datasets, or developing descriptive, predictive, and prescriptive analytics can be substituted for a Bachelor's degree. A PhD from an accredited college or university in a quantitative discipline can be substituted for four (4) years of experience.