Data Scientist in Data and Machine Learning

6 days ago

West Midlands Combined Authority, United Kingdom Digital Waffle Full time
BI Engineer (Azure, Python, SQL)

Location: Remote

Salary: £45k - £48k

Opportunity

Digital Waffle is seeking a talented BI Engineer to join their team in an exciting and innovative industry. The successful candidate will work alongside other Data and Analytics teams to build a newly created BI and Data Engineering division.

This role will be instrumental in leveraging data to ensure a fully integrated approach to data-driven decision-making and also business strategy.

Day-to-day tasks
  1. Work across the group and with the divisions' data partners to leverage all available data, building out the group's database, reporting and insights capabilities to enable data-led decision making.
  2. Design, develop and maintain scalable, user-friendly and automated systems, data solutions and reporting that will support our analytical and business needs.
  3. Support the insight team with visualisations, reporting and stakeholder engagement through monthly/quarterly/annual departmental reviews.
  4. Use data science/statistical modelling tools in collaboration with the analyst team and departmental stakeholders to develop Artificial Intelligence (AI) and Machine Learning (ML) functions and analytics.
Required skills and experience for the role
  1. Demonstrable experience or certification(s) within the field of data engineering, data science, and/or business analysis.
  2. Practical experience of database design and data transformation, supported by ETL processing.
  3. Managing data pipelines & orchestration that enable the transfer and processing of data (Databricks, Microsoft Fabric, Alteryx, Snowflake, Apache).
  4. Coding and programming, capable of working through complex problems with others from the team, adapting quickly to changing market trends and business needs (SQL, Python or R).
  5. Utilising cloud-based services and corresponding toolsets (Microsoft Azure, GCP, AWS or similar).
  6. Comfortable handling structured and unstructured data, from 1st, 2nd, and 3rd party data sources, combining multiple data sets, to produce a cohesive picture of performance and derive insights and recommendations.

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