About us

Insurwave is where insurance buyers consolidate and visualise their data to understand their risk and make smarter transfer decisions.

Our platform offers an integrated insurance management experience, from collecting and consolidating risk data to its distribution to all parties involved, keeping everyone in the insurance value chain connected and up-to-date.

In one place, companies buying and selling risk can harness insightful data, view business exposure changes in real-time and automate time-consuming tasks to focus on what they do best.

Our People - Insurwavers

The success of Insurwave is due to the commitment of our people to delivering the quality and service our clients need to be successful. The commitment to quality and service are paramount to set us apart from our competitors. To maintain and grow the business, we must continue to exceed and improve upon our high standards.

Insurwavers are high-performing, innovative, collaborative and multi-cultural people focused on delivering our vision in line with our behaviours and unified values. We maximise individual potential by developing our people and reinforcing our culture.

Insurwavers' voices are heard and valued.

Our Values

Client First

Insurwavers put Client's experience first

Think Big

Insurwavers build with ambition

Team Players

Insurwavers have each others back

About the role

About the Role:

As a Senior Data Scientist, the candidate will work closely with Product and Engineering teams and will play a significant role in team responsible for building the AI and Analytics capabilities that power the Insurwave platform. The team is self-sufficient and fully responsible for design, development, testing, delivery, and support of the solutions. The candidate will be working across the full ML development lifecycle: data wrangling, model build, model evaluation, model deployment and model monitoring. The candidate will actively participate in these processes and will be leading and making technology and design decisions. The candidate will build solutions aligned with company-wide rules of engagement and standards and will work closely with Head of Data and AI to improve them when needed. The candidate will support team members growth and promote an open, learning culture.

Key responsibilities:

Lead and manage complex data science projects from conception to deployment, including defining project scope, timelines, and deliverables.
Build high-performing AI/ML models that meet business-defined performance metrics, ensuring scalability, efficiency, and reliability.
Develop and deploy production-ready data science code and models using fully automated processes, including Continuous Integration/Continuous Deployment (CI/CD) and testing frameworks.
Continuously improve the performance, security, architecture, and maintainability of owned services through iterative development and optimization.
Work closely with data analysts, data engineers, data scientists, and other business areas to ensure solutions are aligned with requirements, delivered according to plans, and developed to expected quality and security standards.
Work closely with AI product manager to review model monitoring reports and analyse datasets in order to inform model improvement needs.
Provide technical leadership and mentorship to junior data scientists, fostering a culture of learning, collaboration, and continuous improvement.
Ensure the team adheres to defined best practices, standards, and processes, promoting excellence in technical execution and project delivery.
Stay current with the latest advancements in data science and machine learning research and propose innovative solutions to address business challenges.

Skills & Experience

Proven experience in Data Science and Machine Learning, combining excellent academic research with significant commercial impact.
Demonstrated ability to deliver data science projects in production environments, preferably cloud-based systems.
Strong problem-solving skills with the ability to quickly grasp new business concepts and domains.
Experience in leading ML Operations, including model deployment, monitoring, and optimization.
Deep understanding of common machine learning problems and solid knowledge of mathematical foundations of ML algorithms.
Proficiency in Deep Learning Architectures (e.g., MLP, RNN, CNN) and popular frameworks (e.g., TensorFlow, PyTorch, Keras).
Expertise in multiple open-source machine learning libraries (e.g., scikit-learn, Pandas, NumPy, Matplotlib, seaborn, Spacy, NLTK, transformers, Hugging Face, pymupdf).
Practical experience solving NLP and document extraction problems.
Ability to create compelling stories and visualizations with data to communicate insights effectively.
Proficiency in Python coding and version control systems.
Strong coaching and development skills, with the ability to mentor junior team members effectively.
Collaborative mindset with experience working across functional teams and departments.
Willingness to learn new technologies and adapt to evolving industry trends.

Desirable

Knowledge of Azure ecosystem
Experience in working with MLOps frameworks (preferably Azure-ML)
Experience with CI/CD pipelines, Test-Driven Development and pipeline automation.

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