Senior Associate QC Data Analytics

Purpose The role will be responsible for providing data analytics support to enable data-driven decision making, building foundational capability to provide visibility into business processes and implementation of advanced analytics solutions to drive value. Additionally, the role may involve carrying out additional work functions that are not described in this specification but are associated with their role. Responsibilities

1. Fostering a culture of data analytics and data visualisation through sharing of best practices and training of QC staff.
2. Support the definition and advancement of a self service reporting model.
3. Understanding business needs and developing practical data-driven solutions to meet those needs.
4. Identify, manage and implement data analytics solutions for continuous improvement projects related to laboratory operations.
5. Support to cross functional analytics projects. Building relationships with key business stakeholders, defining user requirements.
6. Spotfire and Tableau reports development and maintenance.
7. Development and execution of report validation protocols.
8. User Acceptance Testing.
9. Development activities in SharePoint and Microsoft teams.
10. Liaise with IT teams for troubleshooting activities.
11. Write SQL queries and provide support for routine data pull request tickets.
12. Performing ad hoc data mining and analysis.
13. Exploring and evaluating new digital tools and techniques to advance operational capabilities.
14. Predictive Model Development. Assist in the development of a wide-range of innovative data and analytics solutions, from descriptive to machine learning based. Creating analytical models and visualisations that make business and process data actionable.
15. Provide support as required for design, build and validation activities related to LIMS downstream reporting application business reports.

1. Hold a third level qualification in data analytics, computer science or related field.
2. Proficiency in programming languages, with emphasis on SQL, Python, and/or R.
3. Experience with data visualisation tools or packages, such as Spotfire, Power BI or Tableau.
4. Familiarity with common laboratory operations systems, such as LIMS and LMES an advantage.
5. Experience with advanced statistical/analytical techniques and machine learning algorithms an advantage.
6. Excellent Organisational Skills and Time Management.
7. Strong Communication Skills.
8. Ability to work under minimal supervision.
9. Ability to identify and manage competing priorities.