Data Scientist Quality Engineer

The Data Scientist Quality Engineer will combine expertise in data science and quality assurance to ensure accuracy, reliability, and performance of data-driven products and models. The position will analyze data to draw insights, patterns, and conclusions that will enable the business to improve its products, services, and processes. The Data Scientist Quality Engineer requires knowledge of tools and methods, including statistics, artificial intelligence, and machine learning and must be adept at handling large, complex datasets and developing and applying robust analytical processes, including data cleaning, data interpretation models, forecasting, pattern identification, and trend analysis.

RESPONSIBILITIES

• Leadership and Direction

Exemplify MonoSol's Next Level Leadership behaviors of live our values, effective communication and take action.

• Advanced and Predictive Analytics

Run advanced and predictive analyses and perform model assessments, validation, and enhancement activities, using predictive analytics' software tools and functionalities.

• Data Exploration

Conduct research and select relevant information to enable analysis of key themes and trends using primary data sources and business intelligence tools.

• Data and Analytics Strategy

Make recommendations to improve data and analytics systems and platforms, contributing to the continuous improvement and refinement of data and analytics strategy.

• Performance Improvement through Business Intelligence

Support creation of machine learning algorithms by applying standard statistical analysis or data preparation methods.

• Data Architecture

Consult and educate stakeholders on methods for streamlining and standardizing data recording to ensure quality and accuracy.

• Data Collection and Analysis

Conduct research using primary data sources, and select information needed for the analysis of key themes and trends.

• Insights and Reporting

Contribute to the preparation of various data and analytics reports.

• Continuous Improvement

Review existing operations in own area of work and implement innovation processes to generate new ideas and ensure continuous improvements are delivered.

• Personal Capability Building

Develop own capabilities by participating in assessment and development planning activities as well as formal and informal training and coaching; gain or maintain external professional accreditation, where relevant, to improve performance and fulfill personal potential. Maintain an in-depth understanding of technology, external regulation, and industry best practices through ongoing education, attending conferences, and reading specialist media.

TASKS

• Data Collection and Processing: Gathering data from various sources, cleaning, and preprocessing it to make it usable for analysis.
• Data Quality Assurance: Ensuring the accuracy, completeness, and consistency of data by implementing data quality standards and processes.
• Data Visualization: Creating visualizations and dashboards to present data insights in an easily understandable format.
• Continuous Improvement: Continuously refining data processes and models based on new data and feedback.
• Monitoring and Reporting: Regularly monitoring data quality and generating reports to track performance and identify areas for improvement.
• Data Analysis: Using statistical and machine learning techniques to analyze data and extract meaningful patterns and insights.
• Developing Data Models: Creating and refining data models to support business decisions and predictive analytics.
• Deploying Models: Implementing machine learning models into production systems to ensure they provide real-time insights and predictions.
• Data Governance: Ensuring data quality, security, and compliance with relevant regulations.

Qualifications

• Bachelor's Degree in Engineering or equivalent (required)
• 3+ years of relevant experience in a manufacturing environment Six Sigma Green belt
• Ability to manage complexity and ambiguity required
• Effective communication across all levels of the organization required

Closing

The above statements are intended to describe the general nature and level of the work being performed by employees assigned to this position. This is not intended as an exhaustive list of all responsibilities, duties, and skills required. MonoSol, LLC reserves the right to make changes to the job description whenever necessary.

Disclaimer

As part of MonoSol, LLC’s employment process, finalist candidates will be required to complete a drug / alcohol test, physical, and background check prior to employment commencing. MonoSol, LLC is an equal opportunity employer. All qualified applicants will be considered without regard to race, national origin, gender, age, disability, sexual orientation, veteran status, or marital status.