Senior Data Scientist

Penske•Reading, PA

51d

About The Position

The Senior Data Scientist transforms complex data into business value through advanced analytics, artificial intelligence, and machine learning. The role leads the design, development, and deployment of scalable analytical solutions for high-impact business problems and data-driven decision-making. Work includes evaluating data sources; developing predictive models, statistical algorithms, AI/ML solutions, and proofs of concept; and translating research into production-ready capabilities. The position also builds data pipelines and analytical datasets for modeling and AI applications, with a focus on scalable, reliable solutions. The Senior Data Scientist provides technical leadership and guidance to junior data scientists, collaborates with cross-functional teams, and leads multiple medium- to large-scale projects from concept through implementation. The role partners with business leaders and subject matter experts, communicates analytical findings and recommendations to technical and non-technical audiences, and measures the business impact of solutions. It promotes responsible AI practices, including fairness, transparency, and explainability, and supports ongoing improvement of data science capabilities and business outcomes.

Requirements

Bachelor's degree required, preferably in Engineering, Data Science, Computer Science, Statistics, Mathematics, or a related field.
At least 6 years of experience designing and building data science, machine learning, or AI applications using structured or unstructured datasets.
Experience using programming and statistical tools such as Python, R, SQL, Dataiku, AWS, Oracle, or similar to manipulate data and generate insights from large datasets.
Demonstrated experience with multiple machine learning techniques, such as logistic regression, decision trees, random forests, clustering, and other predictive modeling methods.
Demonstrated ability to evaluate AI and machine learning solutions from a systems perspective and apply systems thinking.
Ability to effectively prioritize and manage multiple projects simultaneously.
Regular, predictable, and full attendance is an essential function of the job.
Willingness to travel as necessary, work the required schedule and at the specified location, complete a Penske employment application, and submit to a background investigation and drug screening.
Ability to meet the physical and mental demands of the role, with reasonable accommodations where applicable; these include reading, communicating, remembering and analyzing information, and understanding instructions or guidelines.
Ability to stand, walk, and sit; frequently use hands to touch, handle, and feel and reach with hands and arms; and occasionally lift and/or move up to 25 lbs/12 kg.
Vision abilities include close vision, distance vision, peripheral vision, depth perception, and adjusting focus.

Nice To Haves

Experience working with cloud platforms and data environments such as AWS and Oracle.
Experience working with large-scale data processing, data engineering, and cloud-based analytics.
Experience applying AI techniques, including generative AI, large language models, natural language processing, or other AI technologies.

Responsibilities

Identify source data and build data models to solve complex business problems.
Extract, transform, clean, validate, and prepare structured and unstructured data; monitor data quality.
Collaborate with IT to build scalable data pipelines and analytical datasets for modeling and analytics.
Perform feature engineering and exploratory data analysis; develop data quality metrics and visualizations.
Design, develop, deploy, and monitor statistical and machine learning models, selecting appropriate methods and algorithms.
Design and evaluate predictive analytics solutions, conduct statistical analyses and experiments, and validate hypotheses to support business decisions.
Develop prototypes and proofs of concept and operationalize analytical solutions for production use.
Monitor model performance, address model drift, and drive continuous improvement and business outcomes across the AI/ML lifecycle.
Apply forecasting, classification, clustering, natural language processing, sentiment analysis, generative AI, agentic AI, and large language models where appropriate.
Lead multiple medium- to large-scale data science initiatives from concept through implementation; translate business challenges into analytical solutions and define technical approaches and project methodologies.
Establish best practices for data science development, documentation, and model governance.
Partner with Enterprise Engineering, Advanced Analytics, HRIT, and other cross-functional teams to deliver enterprise solutions.
Mentor and provide technical guidance to junior data scientists.
Evaluate emerging technologies and recommend improvements to analytical solutions and data science practices.
Partner with business leaders and subject matter experts to identify data-driven opportunities; communicate methods, insights, and recommendations to varied audiences.
Present findings through data storytelling and visualization; measure and communicate business impact using appropriate performance metrics.
Create technical documentation and project deliverables, facilitate discovery sessions, and support solution design.
Stay current with data science, machine learning, AI, and cloud technologies; build expertise in business domains and enterprise data assets.
Promote responsible AI through fairness, transparency, and explainability; share knowledge and best practices and identify opportunities to improve analytical capabilities and business outcomes.
Complete other projects and tasks as assigned.