1. Develop and implement machine learning algorithms for the prediction and assessment of risks associated with properties, focusing on factors such as environmental, geographical, and structural considerations.
2. Collaborate with cross-functional teams, including data engineers, domain experts, and stakeholders, to gather relevant data and insights for the enhancement of risk prediction models.
3. Utilize advanced statistical and machine learning techniques to analyze large datasets and identify patterns, trends, and anomalies related to property risks.
4. Design and deploy predictive models that assess the vulnerability of properties to various risks, providing actionable insights for risk mitigation and management strategies.
5. Evaluate and refine existing risk models, incorporating feedback and new data sources to continuously improve prediction accuracy.
6. Stay current with industry trends, emerging technologies, and best practices in data science and risk management to ensure the application of cutting-edge methodologies.
7. Collaborate with IT and data governance teams to ensure data quality, security, and compliance with regulatory standards.
8. Communicate complex technical concepts and findings to non-technical stakeholders in a clear and understandable manner.
9. Mentor junior data scientists and contribute to the professional development of the team.
1. Master's or Ph.D. in Data Science, Statistics, Computer Science, or a related field.
2. Proven experience (from 7 years) working as a data scientist with a focus on risk prediction and machine learning.
3. Strong proficiency in programming languages such as Python or R.
4. Experience with machine learning frameworks (e.g., TensorFlow, PyTorch) and data manipulation tools (e.g., Pandas, NumPy).
5. Solid understanding of geospatial data, property data, and risk assessment methodologies.
6. Excellent problem-solving skills and the ability to work independently and collaboratively.
7. Strong communication and presentation skills.
8. Experience in the insurance or real estate industry is a plus.
1. Develop and implement accurate and dynamic pricing models for insurance premiums using advanced statistical and machine learning techniques.
2. Collaborate with actuaries, underwriters, and product managers to understand business requirements and align pricing models with strategic goals.
3. Analyze large and diverse datasets to identify relevant risk factors and pricing variables, ensuring models are aligned with market trends and regulatory requirements.
4. Implement usage-based insurance (UBI) models and telematics data to create personalized pricing structures for policyholders.
5. Utilize predictive analytics to assess customer behaviors, market dynamics, and emerging trends to inform pricing strategy.
6. Conduct model validation, testing, and calibration to ensure the accuracy and reliability of pricing models.
7. Collaborate with IT and data engineering teams to integrate pricing models into operational systems and ensure scalability.
8. Stay abreast of industry developments, advancements in predictive modeling, and emerging technologies to continuously enhance pricing methodologies.
9. Provide insights and recommendations to senior leadership based on data analysis and modeling results.
1. Master's or Ph.D. in Data Science, Statistics, Actuarial Science, or a related field.
2. Proven experience (at least 5 years) working as a data scientist with a focus on insurance premium pricing.
3. Strong proficiency in programming languages such as Python or R.
4. Experience with machine learning frameworks (e.g., TensorFlow, PyTorch) and statistical modeling tools.
5. Understanding of insurance industry regulations, underwriting principles, and actuarial concepts.
6. Excellent analytical and problem-solving skills, with the ability to translate business requirements into quantitative models.
7. Strong communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.
8. Experience working with telematics data and usage-based insurance models is a plus.
1. Strategic Partnership Development:
• Identify and establish strategic partnerships with key stakeholders, including insurance companies, reinsurers, insurtech firms, and other potential collaborators to expand the reach and adoption of the predictive insurance platform.
2. Market Analysis and Opportunity Identification:
• Conduct thorough market analysis to identify new business opportunities and market trends. Utilize insights to drive the development and enhancement of the predictive insurance platform.
3. Client Relationship Management:
• Cultivate and maintain strong relationships with existing clients and partners. Understand their needs and challenges, ensuring the predictive insurance platform aligns with their business objectives.
4. Sales and Revenue Growth:
• Drive sales and revenue growth by actively promoting the predictive insurance platform to potential clients. Develop and execute effective sales strategies to meet or exceed revenue targets.
5. Customization and Solution Positioning:
• Work closely with clients to understand their specific requirements and tailor the predictive insurance platform to meet their needs. Effectively position the platform as a comprehensive solution that adds significant value to their business.
6. Collaboration with Product and Development Teams:
• Collaborate with product managers and development teams to align the platform's features and capabilities with market demands. Provide valuable input based on client feedback and industry insights.
7. Industry Thought Leadership:
• Establish a strong presence as an industry thought leader by staying abreast of industry trends, attending conferences, and contributing to thought leadership pieces. Use this expertise to position the predictive insurance platform as an innovative solution.
8. Contract Negotiation:
• Lead contract negotiations with clients and partners. Ensure agreements align with business goals, mitigate risks, and foster long-term relationships.
9. Cross-Functional Collaboration:
• Work closely with cross-functional teams, including marketing, legal, and customer support, to ensure a seamless client onboarding process and ongoing client satisfaction.
10. Feedback Collection:
• Act as a liaison between clients and internal teams, collecting valuable feedback and insights to inform product enhancements and improvements.
11. Training and Onboarding:
• Provide training and onboarding support to clients, ensuring a smooth transition to the predictive insurance platform. Address any issues or concerns to guarantee a positive user experience.
1. Education:
• Bachelor's degree in Business, Marketing, or a related field. MBA is a plus.
2. Experience:
• Proven experience (at least 5 years) in business development, sales, or partnerships within the insurance or insurtech industry.
3. Industry Knowledge:
• Strong understanding of the insurance industry, with knowledge of predictive analytics, machine learning, and emerging technologies in the insurtech space.
4. Sales Acumen:
• Demonstrated success in driving sales and revenue growth, with a track record of exceeding targets.
5. Communication Skills:
• Excellent communication and presentation skills. Ability to articulate complex concepts in a clear and compelling manner.
6. Relationship Building:
• Strong networking and relationship-building skills, with the ability to establish rapport with C-level executives and decision-makers.
7. Strategic Thinking:
• Strategic mindset with the ability to identify and capitalize on market opportunities. Experience in developing and executing successful business strategies.
8. Adaptability:
• Ability to thrive in a dynamic and fast-paced environment. Willingness to adapt to changing market conditions and evolving business needs.
9. Problem-Solving:
• Strong problem-solving skills with the ability to address client challenges and propose effective solutions.
10. Team Collaboration:
• Proven ability to collaborate effectively with cross-functional teams to achieve common goals.
11. Travel:
• Willingness to travel as needed for client meetings, conferences, and industry events.
