EMPLOYER

Penske Truck Leasing Co, L.P.

TITLE:

Data Scientist - (Multiple Openings)

DUTIES:

Assist in identifying appropriate data sources to answer business question. Extract, blend, cleanse, and organize data. Visualize data. Identify (and ameliorate) outliers and missing or incomplete records in the data. Participate in the identification of appropriate techniques and algorithms for building model. Create and test model. Build application and embed model. Engage in best practices discussions with other team members regarding modeling activities. Discuss project activities and results with team. Help in the documentation and presentation of project stories. Collaborate with various stakeholders to identify business problems. Help conduct ROI analysis to determine project feasibility. Help build business case to solve such business problems. Other projects as assigned by the supervisor. Hybrid work permitted two (2) days per week within a reasonable commuting distance of Reading, PA.

SCHEDULE

40 hours per week, Monday through Friday

LOCATION

Penske Truck Leasing Co, L.P., 2675 Morgantown Road, Reading, PA 19607

REQUIREMENTS:

Main Education and Experience:

Master's degree (or foreign equivalent) in Business Analytics, Engineering, Operations Research, Statistics, Applied Mathematics, Computer Science, Earth Sciences, or related field plus three (3) months of experience as a Data Science Intern, Research Assistant, or any occupation in which the required experience was gained.

Special Skills:

Plus demonstrated experience in:

Designing and building machine learning applications using either structured or unstructured datasets.
Programming using Python, R or other high-level scripting languages.
Machine learning techniques including logistic regression, decision trees, or random forest clustering.
Data Analytics.
Machine learning.
Statistical modeling.
Supervised learning.
Statistical computing packages.
Ability to collect and analyze complex data.
Ability to translate data insights into action.
Ability to analyze large structured or unstructured datasets, including datasets exceeding 1,000,000 records or multi-variable scientific datasets.
Ability to design experiments or analytical frameworks to extract insights from complex phenomena.
Ability to analyze time-series, sensor, geospatial, or telemetry datasets.
Ability to apply quantitative or scientific modeling techniques, such as simulation, predictive modeling, or statistical inference.
Ability to design or building machine learning applications using supervised learning algorithms (logistic regression, decision trees, random forests, gradient boosting).
Ability to prepare technical reports or presentations to communicate analytical findings.

CONTACT:

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