Enhance our ability to find performance insights through the use of advanced data science metholodogies, tools, and approaches to leverage artificial intelligence technologies and techniques.
- The Data Scientist (Engineer) supports the data and analytics value chain and GOO dashboards, providing real-time access to performance data to support the organizations ability to find insights from our data.
- Support the Analytics Community of Practice
- Sustain and improves GOO Intelligence
- Works collaboratively across the Performance sub-function so that the analytical tools delivered meet the needs of the Analyst.
- Data Scientists (Engineers) Creates machine learning models that deliver value to all areas of Operation via modern statistical techniques, including: Regression, Support Vector Machines, Regularization, Boosting, Random Forests and other Ensemble Methods, leveraging high-level languages, such as R, Python, Perl, Ruby and Scala
- Deploy models via APIs into applications or workflows
- Deliver functional proof-of-concepts for GOO
- Discover hidden insights/embedded patterns to enable business stakeholders to make more informed decisions
- Collaborate with data architects and developers to define architectures and select technologies
- Data Scientists (Engineers) contribute to the overall prescriptive strategy and goal development for Machine Learning/ AI automation
Experience and Job Requirements:
- Higher education degree (Masters, or PhD) in Business, MIS, Mathematics, or Engineering preferred
- A deep knowledge of performance management and reporting systems, machine learning, AI, etc.
- A strong working knowledge of various systems and processes utilised within our industry
- Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases.
- Experience building and optimizing 'big data' data pipelines, architectures and data sets.
- Strong analytic skills related to working with structured and unstructured datasets.
- Build processes supporting data transformation, data structures, metadata, dependency and workload management.
- A successful history of manipulating, processing and extracting value from large datasets.
- Working knowledge of message queuing, stream processing, and highly scalable 'big data' data stores.
- Experience using the following software/tools:
- Big data tools: Hadoop, Spark, Kafka, etc.
- Relational SQL and NoSQL databases, including Postgres and Cassandra.
- Hands on experience in Azure and AWS cloud services: Databricks, EC2, EMR, RDS, Redshift, ADS.
- Data pipeline and workflow management tools: Airflow, Logic Apps, Data Factory, etc.
- O365 data tools: PowerApps, Flow, SharePoint Online, etc.
- Experience with data visualization tools for the design and development of dashboards, reports, and front-end visualizations: Power BI, Spotfire, etc.