Data Analyst
2 - 5 years
Charlotte, NC
Full-time
Hybrid
1 month ago
About the role
Role Overview
Confidential client is hiring a Data Scientist to join a multidisciplinary actuarial and data science team focused on designing and delivering analytical insights that improve core actuarial processes. This role uses statistical modeling, machine learning, and data engineering to build and maintain loss models and other predictive solutions. The ideal candidate is experienced with Python and/or R, SQL, the end-to-end model lifecycle, and communicating results to technical and non-technical partners.
What You’ll Do
Location & Work Arrangement
confidential client offers a Hybrid or Remote arrangement depending on experience and role fit. Candidates living near an office (Columbus, OH; Chicago, IL; Hartford, CT; or Charlotte, NC) are expected to be in-office approximately 3 days per week (Tuesday–Thursday). Candidates not near an office may be eligible for a remote arrangement, with an expectation to come into an office as business needs require.
Minimum & Preferred Qualifications
Compensation
The annualized base pay range for this role is:
This range is primarily based on external market data. Actual base pay may vary based on experience, proficiency, demonstrated competencies, and other job-related factors. Total compensation may include additional elements such as bonuses, long-term incentives, and recognition programs.
Equal Opportunity & Hiring Transparency
CareerTakes and our client are Equal Opportunity Employers committed to building a diverse and inclusive workforce. We prohibit discrimination or harassment of any kind. To support a fair and efficient hiring process, AI tools may be used to assist with application review or resume screening. These tools do not replace human decision-making . Final hiring decisions are made by people.
Confidential client is hiring a Data Scientist to join a multidisciplinary actuarial and data science team focused on designing and delivering analytical insights that improve core actuarial processes. This role uses statistical modeling, machine learning, and data engineering to build and maintain loss models and other predictive solutions. The ideal candidate is experienced with Python and/or R, SQL, the end-to-end model lifecycle, and communicating results to technical and non-technical partners.
What You’ll Do
- Create statistical models, algorithms, and machine learning solutions to enhance traditional actuarial processes and loss modeling assumptions
- Apply Python and/or R to develop, maintain, and support production and research models across lines of business
- Design and implement end-to-end model lifecycle components: requirements, development, validation, deployment, monitoring, and documentation
- Extract and profile data using SQL and work with data engineering partners to ensure model-ready datasets
- Collaborate with actuaries, product partners, data engineers, and stakeholders to align technical solutions with business strategy
- Translate model results and limitations into clear presentations and actionable recommendations for non-technical audiences
- Contribute to long-term tools and frameworks that scale modeling and analytic capabilities
- Stay current with research and state-of-the-industry techniques, proposing innovations where they add business value
Location & Work Arrangement
confidential client offers a Hybrid or Remote arrangement depending on experience and role fit. Candidates living near an office (Columbus, OH; Chicago, IL; Hartford, CT; or Charlotte, NC) are expected to be in-office approximately 3 days per week (Tuesday–Thursday). Candidates not near an office may be eligible for a remote arrangement, with an expectation to come into an office as business needs require.
Minimum & Preferred Qualifications
- 2+ years of relevant industry or applied data science experience recommended
- Bachelor’s degree required; preference for Master’s in Statistics, Applied Mathematics, Data Science, Computer Science, Actuarial Science, or a related analytical field
- Preferred: progress toward relevant actuarial or professional credentials (e.g., FCAS, FSA, CSPA)
- Hands-on experience in statistical modeling, inference, and building machine learning algorithms using Python and/or R
- Proficient in SQL and comfortable navigating relational databases to extract and transform attributes for modeling
- Familiarity with model deployment and reproducibility tools; experience with Unix, Git, Shiny, and R Markdown is a plus
- Demonstrated experience across the end-to-end modeling lifecycle (requirements, development, validation, monitoring)
- Strong written and verbal communication skills; able to present technical results to non-technical stakeholders
- Self-motivated, results-oriented, and effective as a collaborative team member with strong ownership of deliverables
- Candidates must be authorized to work in the U.S. without company sponsorship. confidential client will not support the STEM OPT I-983 Training Plan endorsement for this position.
Compensation
The annualized base pay range for this role is:
- $90,160 - $135,240
This range is primarily based on external market data. Actual base pay may vary based on experience, proficiency, demonstrated competencies, and other job-related factors. Total compensation may include additional elements such as bonuses, long-term incentives, and recognition programs.
Equal Opportunity & Hiring Transparency
CareerTakes and our client are Equal Opportunity Employers committed to building a diverse and inclusive workforce. We prohibit discrimination or harassment of any kind. To support a fair and efficient hiring process, AI tools may be used to assist with application review or resume screening. These tools do not replace human decision-making . Final hiring decisions are made by people.
- If you have questions about how your data is used, please contact us directly.
Skills
Software DevelopmentInformation Technology