Data Scientist - Machine Learning/AI (Spatial Data)

Data Scientist - Machine Learning/AI (Spatial Data)

Johannesburg (or remote within RSA, with occasional travel)

Permanent

About the Company:

An entrepreneurial and forward-thinking company specializing in utilizing geospatial data and technologies to help clients enhance their operational efficiencies and increase profitability. They offer tailored data, insights, and innovative products across various industries, aiming to convert raw data into practical business insights. With expertise spanning over two decades in spatial and telematics domains, they serve sectors like fleet management, logistics, retail, advertising, the public sector, and more. They value partnerships highly, focusing on collaborative relationships to advance the field of geospatial intelligence.

The company has a number of products and companies under its umbrella, creating and building new products and solutions. The team would like to attract energetic, passionate individuals committed to growing with them.

You will join an R&D team and work with them to design new spatial and data-related products and services with and for their clients. This is an unusual position and will take a product-minded individual with varied skills who is passionate about problem solving and solutioning.

It is required that the individual have some background, studies, or working experience in GIS technologies or spatial data.

Key Responsibilities:

1. Data Collection and Management: Collect, extract, clean, and organize data from various sources, including satellite imagery, telemetry data from IoT and mobile devices, public or proprietary datasets, and business data provided by client or partner organizations.
2. Spatial Analysis and Modeling: Perform spatial analyses (via computational geometry algorithms, through geostatistics to machine learning), spatial statistics, and geospatial information modeling to derive insights and support decision-making.
3. Map Design and Visualization: Implement interactive mapping solutions for stakeholders using the company platforms and tools.
4. Development and Automation: Develop programs, scripts, models, and tools (e.g., using Python, R, SQL in particular) to capture functionality for re-use, automate repetitive tasks, and improve efficiency. Integrate AI technologies as required into tools, visualizations, and catalogs to support conversational interactions.
5. Collaboration and Support: Support project planning and execution by providing geospatial insights, skills, and tooling that align with strategic goals.
6. Quality Assurance: Ensure the accuracy and reliability of geospatial analyses and deliverables through thorough quality assurance and validation processes. Regularly review and refine geospatial methodologies to maintain high standards.
7. Innovation and Research: Stay updated on the latest trends, tools, and technologies in geospatial analytics. Proactively identify opportunities to apply innovative geospatial solutions to business challenges.
8. Stakeholder Communication: Present geospatial findings and recommendations to stakeholders in a clear, concise manner. Prepare detailed reports and documentation for internal and external audiences.
9. Project Contribution: Support project planning and execution by providing geospatial insights that align with strategic goals. Contribute to the development of project timelines, resource estimates, and deliverables where geospatial inputs are required.

Requirements:

1. Completed Degree in Computer Science, Informatics, GeoInformatics IT, Stats, or similar.
2. Minimum of 5 years working experience, including Geospatial knowledge, with demonstrable experience being favored.
3. Client management skills, as you will be working directly with clients in the product space building and developing products. You will need to be able to look for opportunities to simplify product & technical design as well as understand spatial-temporal analytics problems of specific domains such as retail and a variety of others.
4. Portfolio of data science projects that you have worked on and can describe.
5. Expertise in geospatial data processing and analysis tools (e.g., spatial and data science extensions/libraries for Python, R, SQL, with capabilities to extend with AI/ML).
6. Experience with Machine Learning techniques and toolkits, including:
7. Model selection.
8. RAG (Retrieval-Augmented Generation), Vector databases, generation of embeddings.
9. Ability to design and test appropriate ML models, apps, and deployments.
10. Knowledge of geospatial databases (e.g., PostGIS, DuckDB or cloud offerings thereof).
11. Experience with web mapping application servers (e.g. GeoServer, ArcGIS) and development tools and frameworks (e.g., Leaflet, OpenLayers, MapLibre) and cloud geospatial software frameworks (e.g. Google Earth Engine).