Who We Are:
Dedicated to making a difference in law enforcement agencies across the U.S., our mission is to transform policing by elevating officer performance with a preventative-based early intervention system. Driven by data science and powered by machine learning, our offering analyzes officer performance data in order to identify potentially problematic behavior. In partnership with the University of Chicago, we’ve developed the world’s largest multi-jurisdictional officer performance database, and the only research-driven, evidence-based early intervention system available in policing today.
We’re also the only provider of a fully integrated, cloud-based Software-as-a-Service (SaaS) platform that simplifies essential policing workflows. This platform is designed to be a single-source solution for all operational needs, driving extensive efficiency gains and providing best-in-class advanced analytics and insights.
Benchmark Analytics provides a comprehensive, all-in-one solution that is advancing police force management through state-of-the-art technology and market-leading data and analytics.
The Role:
We are currently seeking a talented Lead Data Analyst to join us as we develop our data-driven, analytic-focused, and predictive products to help revolutionize the law enforcement sector. In this role, you will work closely with clients to understand and map their data, analyze large datasets, and support the development of data pipelines and ETL processes. You will also play a key role in identifying data quality issues, optimizing ingestion processes, and presenting findings to internal and external stakeholders.
Responsibilities:
- Assist clients in understanding their data to identify the best fit data for Benchmark products and data science needs
- Work with clients to understand data creation processes, document findings, and articulate them to internal stakeholders
- Analyze large file-based datasets using data transformation tools such as Excel, Databrew, and SQL to effectively map data from source systems to target systems
- Continuously optimize data processes to increase efficiency and quality
- Validate received and extracted data for completeness to ensure it meets data science and system requirements
- Develop and maintain documentation for data pipeline workflows, ETL processes, and data dictionaries
- Provide input and feedback on data structures, data quality, and data consistency
- Identify data quality issues, investigate root cause analysis, and work with appropriate teams to resolve them
- Analyze pipeline processes, identify inefficiencies, suggest improvements, and test