Data Scientist

Liberty General Insurance Berhad; Kuala Lumpur; On-site; MYR 150,000 - 250,000; Full time

Job summary

Liberty Mutual Global Risk Solutions, DSS in Malaysia seeks a Senior Data Scientist to join an Agile team and help build in-house AI/ML capabilities from ideation through deployment to field implementation. You will work on end-to-end product cycles involving model development, NLP/GenAI models, and AI product delivery.

The role emphasizes innovative analytics, experimentation culture, and collaboration with business stakeholders to optimize risk pricing and underwriting decisions.

Qualifications

• Experience applying predictive analytics techniques to business problems.
• Strong Python for analytics and model deployment.
• SQL and data wrangling expertise.

Responsibilities

• Apply sophisticated analytics techniques to large structured and unstructured data to generate business insights.
• Build in-house AI/ML products with data scientists, IT, and stakeholders.
• Develop NLP/AI models including LLMs and GenAI to improve risk pricing.
• Test hypotheses ensuring statistical significance for predictive models.
• Translate analyses into visuals for non-technical audiences and explain outcomes.
• Collaborate across teams to tailor analytic solutions to client needs.
• Participate in data science community and cross-functional groups.

Skills

Predictive analytics

Python programming

SQL

Education

Degree in math, CS or statistics

Master’s degree in CS

Job description

The Liberty Mutual, Global Risk Solutions, DSS is seeking a Senior Data Scientist. This team is responsible for solving complex international specialty insurance problems with analytics insights and AI models that deliver business value. The team is looking for a passionate individual who is full of curiosity, open to new ideas, and willing to fail fast and embrace a culture of experimentation. This individual will join our Agile team and play an essential role in building out in-house AI/ML capabilities and be involved in the end-to-end product cycle, from ideation to model development/deployment to field implementation.

Typical projects would involve the following:

• Design and develop Machine learning models to aid AI insights for the Underwriting, Claims and business development domains
• Use and fine-tune large language models to glean valuable insights from different kinds of unstructured data.
• Develop and adopt the best-in-class ML Ops best practices throughout the data science lifecycle.

This level reflects solid knowledge of predictive analytics techniques, while continuing to learn how to apply emerging AI techniques and architectures to solve evolving business needs.

Responsibilities

• Applies knowledge of sophisticated analytics techniques to manipulate large structured and unstructured data sets in order to generate insights to inform business decisions.
• Buildout in-house AI/ML products with other team members, including data scientists, IT developers, and business stakeholders
• Develop NLP and other AI/ML models including LLMs, VLMs and GenAI applications to improve risk selection and pricing.
• Identifies and tests hypotheses, ensuring statistical significance, as part of building and developing predictive models for business application.
• Translates quantitative analyses and findings into accessible visuals for non-technical audiences, providing a clear view into interpreting the data.
• Enables the business to make clear trade-offs between and among choices, with a reasonable view into likely outcomes.
• Customizes analytic solutions to specific client needs.
• Responsible for smaller components of projects of moderate complexity.
• Regularly engages with the data science community and participates in cross-functional working groups.

Qualifications

• Solid knowledge of predictive analytics techniques and statistical diagnostics of models.
• Advanced fluency in Python programming including model deployment. Cloud deployment experience will be a plus.
• Advanced knowledge of SQL and data wrangling is required.
• Ability to demonstrate industry experience taking problems and translating them into analytics / data science solutions is a plus.
• Demonstrated ability to exchange ideas and convey complex information clearly and concisely.
• Has a value-driven perspective with regard to understanding of work context and impact.
• Candidate should possess a minimum of 2-3 yrs. of corporate experience (with a degree in math, computer science or statistics) or have a master’s degree in computer science with some corporate experience in financial services