Data Scientist

The Data Scientist plays a key role in developing, maintaining, and improving OSP’s production data pipeline environment, analytical tools, reporting assets, and business automations. This position supports legacy and production code running across AWS Glue, Azure, Power Automate, Microsoft Dataverse, and related platforms, while also building new data workflows that improve operational efficiency, billing accuracy, reporting reliability, and business decision-making., This role is responsible for ensuring that data moves accurately, reliably, and consistently from diverse source systems into and between OSP’s core data stores, including AWS Redshift and Microsoft Dataverse. The position blends Python development, SQL/data operations, cloud-based pipeline support, analytical tool development, reporting support, and business process automation. The Data Scientist serves as a technical liaison between IT, Data Science, Analytics, Asset Operations, Client Delivery, Billing Operations, and internal business teams., 1. Production Data Pipeline Development & Support

• Maintain, troubleshoot, and improve legacy Python code used in AWS Glue and related production data processes, including Azure Data Factory and Power Automate.
• Monitor recurring pipeline execution and investigate failures, incomplete loads, schema issues, data discrepancies, and downstream reporting or billing impacts.
• Create and enhance pipelines that ingest, transform, validate, and publish production data for operational, billing, reporting, analytical, and automation use cases.
• Support production data processes with appropriate logging, error handling, retry logic, monitoring, and operational documentation.
• Partner with IT, Data Science, Analytics, and business stakeholders to prioritize fixes, enhancements, and new data pipeline requirements.

1. Data Platform Integration, Quality & Controls * Maintain and create pipelines that move information between AWS Redshift, Microsoft Dataverse, source systems, reporting layers, and business applications. * Support synchronization, transformation, reconciliation, and validation of data across OSP’s enterprise data environment. * Build and maintain data quality controls, including row-count checks, schema validation, duplicate detection, null checks, reconciliation routines, exception reporting, and monitoring alerts. * Investigate differences between source, target, reporting-layer, and business application data and recommend appropriate technical or process corrections. * Maintain awareness of downstream impacts to billing, asset operations, Power Platform applications, reporting, client delivery workflows, and operational decision-making.

2. Analytical Tools, Reporting & Business Automations * Develop and support analytical tools that help internal teams evaluate asset performance, billing activity, customer data, operational trends, financial outputs, and other business-critical datasets. * Build and maintain business automations that reduce manual effort, improve process consistency, and support recurring operational, billing, reporting, and client delivery workflows. * Support reporting environments, including AWS QuickSight, Power BI, or similar tools, by preparing datasets, troubleshooting data issues, validating report outputs, and assisting with report enhancements. * Work with business users to understand analytical, operational, and automation needs, translate those needs into technical requirements, and deliver durable, supportable solutions. * Identify opportunities to replace manual spreadsheets, one-off data pulls, and recurring ad hoc processes with governed data workflows, reusable datasets, automated checks, or reporting tools.

3. Operational Support, Documentation & Cross-Functional Collaboration * Provide technical support for asset production data, billing data, utility data, operational datasets, internal reporting, and client-facing deliverables. * Support new data feeds, source integrations, production data intake processes, recurring operational requirements, and business process improvements. * Document pipeline purpose, source and target systems, transformation rules, dependencies, validation logic, failure handling procedures, automation logic, and recurring support requirements. * Maintain clear technical and operational documentation so pipelines, analytical tools, and automations are supportable by IT, Data Science, Analytics, and business operations teams. * Support OSP’s broader data governance and operational excellence expectations through consistent documentation, controls, issue tracking, and maintainable solution design.

• Bachelor’s degree in Data Science, Computer Science, Information Systems, Engineering, Mathematics, Statistics, Economics, or a related quantitative or technical field, or equivalent experience

• 2-5 years of experience in data engineering, data science, analytics engineering, production data operations, business analytics, or a similar technical role
• Strong Python experience, including the ability to read, maintain, troubleshoot, and improve existing production code
• Strong SQL skills, including querying, joins, transformations, validation, troubleshooting data relationships, and investigating data discrepancies
• Experience developing or maintaining ETL/ELT pipelines, production data workflows, business automations, or analytical data processes
• Experience working with structured datasets, relational databases, cloud-based data platforms, and business application data
• Ability to investigate failed jobs, missing records, schema changes, data quality issues, and downstream business impacts
• Familiarity with reporting or dashboarding tools such as AWS QuickSight, Power BI, or similar environments
• Strong analytical and problem-solving skills with high attention to detail
• Effective communication skills with ability to explain technical and data issues to both technical and non-technical audiences
• Ability to work cross-functionally in a fast-paced, operationally focused environment, * Experience with AWS Glue, AWS Redshift, S3, SQL Server, Azure Data Factory, Azure DevOps, or related cloud data services
• Experience with Microsoft Dataverse, Power Platform, PowerApps, Power Automate, or similar business application platforms
• Experience building or supporting internal analytical tools, business automations, reusable datasets, reporting models, or operational dashboards
• Experience supporting asset operations, billing data, utility data, energy data, finance data, or other production business datasets
• Experience maintaining legacy Python jobs or refactoring production data code for reliability, observability, and supportability
• Experience using Git or similar code management tools for production code review, deployment, and change tracking

4.24.2 out of 5 stars 225 W Wacker Dr 6th FL, Chicago, IL 60606 $110,000 - $130,000 a year