Data Scientist

Geologics•Sterling Heights, MI

1d•Hybrid

About The Position

Responsible for designing, building, and maintaining the data infrastructure, including databases, data warehouses, and data pipelines. Collect data from various sources (databases, APIs, etc.) and implement efficient data pipelines to transform raw data into usable formats such as dashboards and associated reports and insights. Ensure that data is stored securely, efficiently, and in a manner that is accessible for analysis and reporting. Ensuring the accuracy, consistency, and reliability of data, implementing data validation and quality control processes. Optimize data pipelines for speed, scalability, and performance, ensuring that data is available when and where it's needed. Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and ensure that data infrastructure meets their needs. Troubleshoot issues with data infrastructure, monitor system performance, and perform regular maintenance to ensure smooth operation. Support configuration control, documentation and change impact analysis on the businesses data architecture model including identification of Authoritative Source of Truth (ASOT) databases, connectors to replicate data from ASOTS to Midas and/or AWSCloud Environments through data transformation within Power BI.

Requirements

Excellent oral and written communications.
Proven ability to work within teams in which your contributions are critical to success.
Exceptional analytic skills with some knowledge of probability and statistics and mathematical programming.
Proficiency with tools such as Power BI, Visual Basic, MS Excel, MATLAB/Simulink, Mathcad, MagicDraw, Midas, AWS/AWS Redshift, SSAS, Alation, Dataiku, Oracle, SQL.
Familiarity with Atlassian Jira or ServiceNow.
Proficiency with server usage and troubleshooting.
Ability to prioritize and self-manage a multi-tasked workload in a fast-paced environment.
Must be able to work independently to research, analyze, and form solutions.
Must be able to translate data into information; resulting in data-driven actions/recommendations.
Proficiency in areas such as probability and statistics, applied mathematics, Mathematical programming (linear, nonlinear, integer), Network analysis, Queuing theory, and Economic evaluation.
Must have ability to work in a cross-functional team environment.
Ability to develop solutions to complex problems.
Ability to make decisions with sound judgment while complying with policies and procedures.
Working knowledge of technical concepts, strategies, methodologies, architectures, and technical standards.
Self-starter with excellent attention to detail and able to stay organized.
AWS Medallion architecture including AWS Redshift.
Data Cataloguing tools like Collibra.
Data Transformation tools like Dataiku.
Dashboarding tools like PowerBI or Tableu.
Collecting Data from various sources.
Troubleshoot issues with data infrastructure, monitor systems.

Responsibilities

Designing, building, and maintaining the data infrastructure, including databases, data warehouses, and data pipelines.
Collecting data from various sources (databases, APIs, etc.) and implementing efficient data pipelines to transform raw data into usable formats such as dashboards and associated reports and insights.
Ensuring that data is stored securely, efficiently, and in a manner that is accessible for analysis and reporting.
Ensuring the accuracy, consistency, and reliability of data, implementing data validation and quality control processes.
Optimizing data pipelines for speed, scalability, and performance, ensuring that data is available when and where it's needed.
Collaborating with data scientists, analysts, and other stakeholders to understand data requirements and ensure that data infrastructure meets their needs.
Troubleshooting issues with data infrastructure, monitoring system performance, and performing regular maintenance to ensure smooth operation.
Supporting configuration control, documentation and change impact analysis on the businesses data architecture model including identification of Authoritative Source of Truth (ASOT) databases, connectors to replicate data from ASOTS to Midas and/or AWSCloud Environments through data transformation within Power BI.
Developing data models and process flows.
Liaising with multiple stakeholders - mainly engineering, to document and/or validate the current state or As-Is business process.
Developing requirements for collection of and follow on use of data to inform the organization.