FBS Data Scientist (Customer & Agent Solutions)
New
F
FBSInsurance analytics
BrazilFull-TimeMiddle
Salary not disclosed
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Job Details
- Languages
- Fluent English communication skills
- Experience
- 3+ years of experience in Data Science, Advanced Analytics, Machine Learning, or a related field.
- Required Skills
- PythonSQLData MiningMachine LearningDatabricksGitHubMLOps
Requirements
- Bachelor's degree in Data Science, Statistics, Mathematics, Business Analytics, Computer Science, or a related quantitative field.
- 3+ years of experience in Data Science, Advanced Analytics, Machine Learning, or a related field.
- Experience with Python; 4–6 years preferred.
- Advanced SQL experience; 7+ years preferred.
- Intermediate experience with Machine Learning.
- Intermediate experience with Statistical Analysis, Hypothesis Testing, and Descriptive Statistics.
- Experience developing and translating analytical solutions into business outcomes.
- Fluent English communication skills.
- Master's degree in a related field is preferred.
- Experience in P&C Insurance, Banking, Financial Services, or another highly regulated industry is preferred.
- Customer service operations, call center environments, call routing optimization, workforce optimization, or contact center analytics experience is preferred.
- Exposure to Databricks and cloud-based analytics platforms, and knowledge of ML Ops, GitHub, model deployment lifecycle management, or supervised learning models are preferred.
Responsibilities
- Develop and deploy predictive and prescriptive analytics models for customer and agent initiatives.
- Apply machine learning, statistical analysis, and data mining to solve business problems.
- Analyze large datasets to identify trends, opportunities, and recommendations.
- Design, build, and maintain scalable, production-ready analytical solutions.
- Partner with business stakeholders to translate objectives into analytical approaches and success metrics.
- Collaborate with Data Engineering, IT, and Analytics teams on model deployment and optimization.
- Use ML Ops practices, version control, and code documentation standards.
- Present findings and recommendations to technical and non-technical audiences.
- Support customer retention, servicing, operational performance, lead optimization, and agent effectiveness.
- Mentor junior team members and contribute to analytics best practices.
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