Contribute to initiatives that drive improved customer experience and client insights through productionilizing machine learning models and building of production-ready and robust ML and AI systems, platforms and services, in many cases collaborating with software engineering teams.
Practical / Technical Knowledge • Strong foundational skills in mathematics and statistics • Strong understanding of Machine Learning Concepts • Strong Software Engineering Discipline (e.g. Source Control, CI/CD) • Experience with major ML frameworks (Tensorflow, PyTorch, scikit-learn) • Experience with building and tuning Deep Learning models • Experience with Big Data Processing Libraries (e.g. Spark, Dask) • Experience working with structured and unstructured data • Experience building end-to-end solutions on Cloud (SalesForce, AWS, Azure) • Experience working with and tuning services in Cloud Analytical Modelling and Solutioning
• Modelling: Required to apply the most appropriate algorithms and/or build novel algorithms / techniques to fit the problem statement. Must take advantage of data science capabilities offered by different cloud/on prem vendors where appropriate to increase project velocity, take advantage of latest start-of-the-art commercialized data science solutions and reduce internal technical debt. • Solutioning: Required to incorporate ML models into software that meets the upstream of downstream system requirements. (i.e., Engineering Batch, Streaming or API based ML pipelines) • Technical Documentation: Required to deliver technical artefacts articulating the architecture and model robustness of Cloud solutions
Project – Mapping Implementation
• Project Pipelining: Ability to work with technical stakeholders to identify new ML/API/Cloud initiatives. Ability to perform rapid EDA / prototyping exercises to help size projects and high-level success criteria • Business Problem Statement Crystallization Required to convert conceptual Insurance DS needs (non-technical) into crystalized problem statements that can be scientifically measured • Academic and Commercial Research: Required to review academic and commercial literature to identify similar models / solutions that can solve for the business problem statement • Data Analysis and Engineering: Ability to apply engineering and math skills to analyze and prepare structured / unstructured data for modelling
Planning and Organizing Skills
• Able to operate independently on projects • Able to create and size high level plans for projects • Able to create and size tasks in alignment with Sprint plans / goals • Able to work on 2-3 concurrent initiatives at a time where needed • Able to run ML projects and manage direct and virtual resources Stakeholder management
• Ability to work with technical stakeholders to identify new ML/API/Cloud initiativesAttends regular stakeholder meetings. • Ensures alignment with key technical stakeholders such as business unit managers. • Actively engages with stakeholders to fulfil and deliver the Information Management objectives.
Preferred Qualification and Experience
Type of qualification: First Degree Field of study: IT and Computer Sciences
Type of qualification: First Degree Field of study: Mathematical Sciences
certifications or professional memberships Proficiency in coding for structured and unstructured Query languages e.g. SQL, Python, R, JSON, C#, Java, C++. Ability to create APIs.
Type of qualification: Honours Degree Field of study: IT and Computer Sciences
Type of qualification: Masters Degree Field of study: IT and Computer Sciences
Information Technology, Actuarial Science, Statistics, Mathematics, Data Science, or related field
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