Data Analytics & Machine Learning Engineer
Role details
Job location
Tech stack
Job description
The ML & Data Services team is focused on maintaining and enhancing StormHarvester's core ML capabilities, working with customers and other teams to apply new ML & data-driven solutions to meet customer's needs, and developing processes to allow customers to get the most value out of their data. This is a pragmatic and delivery-focused role in the use of data, analytics, and ML to deliver predictive outcomes for StormHarvester customers as part of our product. This will involve working with customer data, understanding and appreciating the underlying domain, carrying out analysis, and integrating or developing new techniques for implementation and delivery as part of our product offerings.
This includes feature engineering, applying varying models, testing, and validation, and best practices for use for customers.
You will work within a team of 6-8, but will have ownership over individual projects, while also working within the larger engineering team to test and validate any features, fixes, or updates.
Requirements
- A third level qualification in Data Science, Computer Science, or a data / ML-driven equivalent;
- 3+ years of experience in Data Science, ML Engineering, Data Analytics, or a related speciality, or equivalent knowledge;
- Experience with Python (Pandas, Scikit-learn or equivalent);
- Experience with data exploration and visualisation;
- Strong presentation and communication skills;
- Willingness to engage and work with others as part of team with shared direction;
- Strong work ethic with an understanding that this is a fast-growing company with lots of opportunities to make improvements and to move quickly;
- Ability to review and provide feedback as needed to other teams on areas for improvements and updates;
- Passionate about work, output and quality;
- Can do, problem solving mindset;
- Curious and willing to onward develop and learn in ML / AI area.
DESIRABLE CRITERIA:
- Experience with AWS services (or transferable cloud experience);
- Experience modelling time series data;
- Familiarity with Geospatial (GIS) data;
- Familiarity with MLOps principles;
- Familiarity and experience with agile development in delivery;
- Experience of Continuous Integration/Development and Tooling;
- Experience using LLMs & other AI tools in a forward thinking and ethical way.