Filter Jobs

Experience:

4 years required

Skills:

Statistics, Python, SQL

Job type:

Full-time

• Collaborate with cross-functional teams to identify and prioritize business opportunities that can be addressed through data-driven solutions.
• Extract insights from large and complex datasets using a variety of tools and techniques.
• Develop and deploy predictive models and algorithms using statistical AI/machine learning, deep learning and generative AI modeling.
• Present findings and recommendations to stakeholders in a clear and compelling manner.
• Implement data-driven solutions in various environments, including integration with existing systems and processes.
• Stay up to date on the latest development in data science and bring new idea and technology to the team.
• Monitor the performance of deployed models together with algorithms and continuously improve model accuracy over time.
• Bachelor s or Master s degree in data science, Computer Science, Statistics, or related field.
• 4+ years of experience as a Data Scientist, with a focus on industries.
• Descriptive Analytics.
• Predictive & Prescriptive Analytics (Machine learning, Forecasting, Optimization, choosing the best path).
• Strong programming skills in Python and SQL along with libraries and frameworks for machine learning and statistical test, and their variation among databases.
• Experience with statistical and machine learning techniques.
• Proficiency in optimizing large, complicated SQL statements and code versioning tools such as Git, Mercurial, SVN or others.
• Experience with data visualization tools such as Looker Studio, Tableau, and Power BI.
• Strong problem-solving and communication skills.
• Experience with Cloud technology and databases (Cloud AWS, Azure, GCP).
• Extensive knowledge of Customer relationship management, Customer experience, Next best action, Credit scoring, and Insight & pattern discovery.
• Location: True Digital Park, Bangkok.

Experience:

5 years required

Skills:

Statistics, Finance, Risk Management

Job type:

Full-time

• Bachelor s degree (or equivalent) degree in a quantitative field such as Data Science, Actuarial Science, Statistics, or Mathematics.
• 5+ years of related practical experience, preferably in commercial insurance sector.
• Solid understanding of insurance pricing principles, loss reserving, and risk assessment methodologies.
• Familiarity with insurance industry regulations, standards, and best practices.
• Develop and maintain loss cost models using GLMs and other advanced statistical techniques, incorporating relevant variables and factors for accurate pricing and risk assessment.
• Analyse historical insurance data to identify patterns and trends, and determine the impact of various factors on loss costs.
• Collaborate with underwriting, claims, and finance teams to understand business needs and provide data-driven insights for portfolio management.
• Conduct rate level reviews to ensure appropriate pricing of insurance products, considering risk exposure, market dynamics, and profitability goals.
• Enhance loss cost models over time by incorporating new data sources, refining variables,.
• and exploring innovative modelling techniques.
• Evaluate the impact of pricing strategies, policy changes, and market shifts on portfolio performance, and make recommendations for adjustments, if needed.
• Present findings and recommendations to stakeholders, including senior management and underwriting teams, in clear and concise reports.
• Work closely with other departments including Underwriting, Actuarial, and Risk Management, providing them with the data and insights needed to make evidence-based decisions.
• Functional Competency.
• Excellent analytical and problem-solving skills, with the ability to translate data into meaningful insights and recommendations.
• Strong communication skills to effectively convey complex findings and recommendations to both technical and non-technical stakeholders.
• Attention to detail and ability to work independently, managing multiple projects and deadlines efficiently.
• Strong proficiency in statistical modeling techniques, specifically GLMs, and experience with software tools like R, SAS, or Python.
• Proficiency with data analysis and visualisation tools and platforms, preferably Qliksense, Power BI, Alteryx, etc.
• Educational.
• Bachelor s degree (or equivalent) degree in a quantitative field such as Data Science, Actuarial Science, Statistics, or Mathematics.
• 5+ years of related practical experience, preferably in commercial insurance sector.
• Solid understanding of insurance pricing principles, loss reserving, and risk assessment methodologies.
• Familiarity with insurance industry regulations, standards, and best practices.
• Develop and maintain loss cost models using GLMs and other advanced statistical techniques, incorporating relevant variables and factors for accurate pricing and risk assessment.
• Analyse historical insurance data to identify patterns and trends, and determine the impact of various factors on loss costs.
• Collaborate with underwriting, claims, and finance teams to understand business needs and provide data-driven insights for portfolio management.
• Conduct rate level reviews to ensure appropriate pricing of insurance products, considering risk exposure, market dynamics, and profitability goals.
• Enhance loss cost models over time by incorporating new data sources, refining variables,.
• and exploring innovative modelling techniques.
• Evaluate the impact of pricing strategies, policy changes, and market shifts on portfolio performance, and make recommendations for adjustments, if needed.
• Present findings and recommendations to stakeholders, including senior management and underwriting teams, in clear and concise reports.
• Work closely with other departments including Underwriting, Actuarial, and Risk Management, providing them with the data and insights needed to make evidence-based decisions.
• Functional Competency.
• Excellent analytical and problem-solving skills, with the ability to translate data into meaningful insights and recommendations.
• Strong communication skills to effectively convey complex findings and recommendations to both technical and non-technical stakeholders.
• Attention to detail and ability to work independently, managing multiple projects and deadlines efficiently.
• Strong proficiency in statistical modeling techniques, specifically GLMs, and experience with software tools like R, SAS, or Python.
• Proficiency with data analysis and visualisation tools and platforms, preferably Qliksense, Power BI, Alteryx, etc.