Data manipulation: Be comfortable in querying large datasets, with the ability to help manipulate and construct the right data structures starting with the end in mind. With an eye for data quality to ensure rigour of input data going into models.
Maintenance: Have a good blend of data science with data engineering concepts in order to help monitoring and maintenance of model production pipelines, including implementation of error handling and testing of analytics code.
Stakeholder management: Being able to communicate with different levels of the business and gather their needs
Storytelling: Ability to translate data science concepts into business language and create a compelling storyline from it
Data visualization: Work with others to design, deploy and maintain online tools and dashboards to be consumed by different parts of the business
Data experimentation: Eager to learn and/or challenge existing forecast models and apply cutting edge machine learning techniques
Skills & Requirements
A solid understanding of statistics (hypothesis testing, regressions, random variables, inference)
Comfortable with presenting back to technical and non-technical stakeholders through effective data visualisation and building of reporting frameworks
Experience accessing and combining data from multiple sources and building data pipelines, including a good knowledge of SQL
Comfortable working in a Python data science tech stack (e.g. pandas, NumPy, scikit-learn, PySpark, PyMC3, Dash, Plotly)
The ability to work collaboratively and proactively in a fast-paced environment alongside both scientists, engineers and non-technical stakeholders
A ‘hackers’ mentality, comfortable using open-source technologies.
A basic knowledge of software development lifecycles, engineering, and machine learning practices (Data pipelines, API workflows, CI/CD deployments, DataOps, MLOps)
Qualifications and Experience:
A degree in Computer Science, Physics, Mathematics or a similar quantitative subject