Technical Lead(Earnix developer)

Mandatory skills:

• 3+ years of experience in:
  • Pricing analytics
  • Predictive modeling
  • Data science
  • Insurance/financial services analytics (preferred)
• Strong knowledge of:
  • Statistical analysis
  • Predictive modeling
  • Machine learning concepts
  • Data visualization
• Proficiency in:
  • SQL
  • Python or R
  • Excel
  • Tableau / Power BI

Job description: Key Responsibilities

• Develop, maintain, and optimize pricing and predictive models using statistical and machine learning techniques.
• Analyze large datasets to identify pricing trends, customer behavior, and profitability drivers.
• Support pricing strategy decisions through scenario analysis, forecasting, and simulations.
• Build dashboards, reports, and visualizations to communicate insights to business stakeholders.
• Collaborate with business, underwriting, product, and engineering teams to implement pricing solutions.
• Validate model performance and ensure accuracy, consistency, and compliance with governance standards.
• Conduct A/B testing and impact analysis for pricing changes and product enhancements.
• Automate recurring analytical and reporting processes where possible.
• Translate business requirements into analytical solutions and technical specifications.
• Monitor market trends, competitor pricing, and customer behavior to recommend pricing adjustments.
• Support regulatory and audit requests related to pricing models and data usage.

Required Qualifications

• Bachelor s degree in:
  • Data Science
  • Statistics
  • Mathematics
  • Economics
  • Computer Science
  • Actuarial Science
  • Engineering
  • Related quantitative field
• 3+ years of experience in:
  • Pricing analytics
  • Predictive modeling
  • Data science
  • Insurance/financial services analytics (preferred)
• Strong knowledge of:
  • Statistical analysis
  • Predictive modeling
  • Machine learning concepts
  • Data visualization
• Proficiency in:
  • SQL
  • Python or R
  • Excel
  • Tableau / Power BI
• Experience with:
  • Regression models
  • Classification models
  • Forecasting techniques
  • Data mining and segmentation