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# Data Scientist

- **Company:** Robert Half
- **Location:** Leipsic, DE, United States
- **Contract:** Contract
- **Skills:** Business Analytics Applications, Big Data, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Distributed Computing Environment, Apache Hadoop, Python (Programming Language), Data Streaming, Apache Spark, Data Analytics, Apache Kafka
- **Published:** October 23, 2026
- **Apply:** https://www.dice.com/job-detail/b77945f0-2637-4e3a-8ddd-3f45f6bc7c26

## About the Role

Hands-on experience with Python for data analysis, modeling, and data engineering tasks.
Strong working knowledge of Apache Spark for distributed data processing and large-scale analytics.
Experience using Apache Hadoop technologies in data-intensive environments.
Familiarity with Apache Kafka for ingesting and processing streaming data.
Proven ability to build and support ETL processes for structured and unstructured data sources.
Background in data science, analytics, or a related technical discipline with the ability to solve complex data problems.
Ability to communicate technical concepts and analytical insights effectively to diverse stakeholders., All applicants applying for U.S. job openings must be legally authorized to work in the United States. Benefits are available to contract/temporary professionals, including medical, vision, dental, and life and disability insurance. Hired contract/temporary professionals are also eligible to enroll in our company 401(k) plan. Visit roberthalf.gobenefits.net for more information.

## Description

We are looking for a Data Scientist to support advanced data initiatives in Dover, Delaware. This Long-term Contract position focuses on building scalable data solutions, uncovering actionable insights, and helping stakeholders make informed decisions through reliable analysis. The ideal candidate brings strong technical depth across modern data platforms and is comfortable working with large, complex datasets in a collaborative environment., Design and maintain data pipelines that collect, transform, and prepare large datasets for analytical and operational use.
Develop statistical models and data-driven solutions that help identify trends, improve forecasting, and support business strategy.
Use Python and distributed processing tools to analyze high-volume data efficiently and produce meaningful recommendations.
Work with Spark and Hadoop-based environments to manage large-scale data processing and support advanced analytics workloads.
Integrate streaming and batch data from multiple sources, including Kafka, to ensure timely and accurate data availability.
Partner with cross-functional teams to translate business questions into analytical approaches, technical requirements, and measurable outcomes.
Build ETL workflows that improve data quality, consistency, and accessibility across reporting and analytical platforms.
Document methodologies, assumptions, and results clearly so that technical and non-technical stakeholders can understand key findings.