Data Science Solutions Engineer

Aon is looking for a Data Science Solutions Engineer

Job Responsibilities: 

• Solution Design & Proof of Concepts: Work closely with clients to understand their unique business challenges and design custom solutions using PathWise. You will build and present proof-of-concept models to demonstrate the value and capabilities of our software.
• Technical Pre-Sales Support: Partner with the sales team during the pre-sales cycle to deliver compelling product demonstrations and technical presentations. You will be responsible for showcasing the power of our platform and answering deep technical questions from prospective clients, who are often experienced actuaries and quantitative analysts.
• Performance Optimization: Work directly with clients to analyze their existing actuarial models and guide them in refactoring and optimizing their implementations to take full advantage of the unmatched performance of PathWise enabled by GPUs.
• Advanced Technical Support: Serve as the primary point of contact for complex technical issues reported by clients and internal teams. You will be responsible for diagnosing, troubleshooting, and providing solutions for issues related to financial and actuarial models, system performance, and software configuration.
• Client Onboarding & Training: Lead the technical onboarding and training for new clients, ensuring they have the knowledge and tools to successfully implement and utilize our software.
• Customer Advocacy: Act as the voice of the customer internally. Gather and relay client feedback to the product management and development teams to influence the future direction of the platform.

 

Experience and Qualifications: 

Required

Quantitative Background: Bachelor’s in a quantitative field such as Engineering, Mathematical Finance, Mathematics, Physics, Statistics, or Computer Science.

Statistics, Modeling & Quantitative Analysis: Proven expertise in applying statistical methods and rigorous quantitative analysis. Experience with Monte Carlo Simulation.

Industry Experience: 8+ years of experience in a quantitative analysis and data science role.

Programming Proficiency: Strong programming skills in Python. C#/ C++ a plus.

Exceptional Communication: The ability to distill and explain highly complex topics in parallel computing and quantitative finance to a variety of audiences.

Nice-to-Have (Preferred):

Advanced Quantitative Background: Master’s or PhD in a quantitative field.

Parallel Programming: Solid understanding of parallel processing and high-performance computing (HPC) architecture and GPGPU concepts. Experience with a parallel computing framework like CUDA, OpenCL or OpenMP.

Actuarial Industry Experience: 5+ years of experience in the insurance, reinsurance, or capital markets industry with exposure to complex financial models.

Actuarial Domain Knowledge: Solid grasp of the mathematical and theoretical foundations of actuarial science.

Quantitative Finance Domain Knowledge: Solid grasp of the mathematical and theoretical foundations of quantitative finance (e.g., stochastic processes, derivative pricing).

Progress towards actuarial designation (ASA/FSA) or financial designation (CFA/FRM).

Direct experience with actuarial modeling software (e.g., AXIS, Prophet, RiskAgility).