Python Numerical Computing Engineer

Akur8•Atlanta, GA

About The Position

Akur8 is a fast-growing InsurTech scale-up on a mission to modernize how insurers assess, price, and manage risk. Our next-generation SaaS platform combines transparent machine learning, predictive analytics, and smart product design to turn complex insurance workflows into scalable, data-driven systems. Powered by expert engineering, data, product, and actuarial teams, Akur8 enables insurers to model risk up to 10x faster while maintaining transparency, explainability, and regulatory trust. As our product suite expands, we are building an end-to-end platform supporting pricing, reserving, and forward-looking risk decisions — helping insurers operate efficiently in a complex environment. Recognized globally, Akur8 has been featured in: CB Insights’ Insurtech 50 (2025) CNBC’s InsurTech Top 150 (2025) InsurTech100 Global Insurtech Top 100 (2025) Professional Equality Index 97/100 (2025) With 40+ nationalities across 8 global offices, we serve 320+ clients across 4 continents while maintaining a strong engineering and product culture. We are proud to be an equal opportunities employer, fostering an inclusive environment where diverse perspectives help shape better products and decisions. We’re looking for a Python engineer who loves translating complex mathematical and actuarial models into high-performance code and optimizing numerical computations. You’ll join a newly formed team building a cloud-native product for the insurance industry. Working closely with actuarial and data experts, this role focuses on the accurate, efficient implementation of domain-specific business rules, with a strong emphasis on numerical performance. Code quality matters: implementations must be stable, readable, testable, and highly performant. A strong understanding of vectorized operations, parallelization, and efficient data structures is essential. This role focuses on optimization and numerical libraries rather than system architecture or broad full-stack development.

Requirements

3+ years of experience using Python for numerical computing
Proven experience in writing production quality Python code
Strong ability to turn mathematical, financial, or actuarial concepts into efficient code
Ability to write clean, modular, testable Python
Hands-on experience improving performance in analytical or numerical workloads
Solid understanding of vectorized computing (e.g. NumPy), numerical accuracy, stability, and performance trade-offs
Experience with packaging, virtual environments, and dependency management
Comfort with agile workflow frameworks and management tools such as Jira
High level of familiarity with git, GitHub, branching strategy, resolution of merge conflicts
Constructively receives and gives peer review for code review and pull requests
Works independently from stories and specifications written by non-engineering subject matter experts.
Strong written and spoken English

Nice To Haves

Exposure to actuarial or insurance data
Familiarity with reserving workflows, loss triangles, or claims data
Experience translating formulas or models from R into Python
Some exposure to C#/.NET or containerized environments

Responsibilities

Translate actuarial requirements (including prototype R code) into efficient, maintainable Python
Optimize complex mathematical operations to run 10× faster using vectorization and algorithmic improvements
Implement and refactor numerical Python functions with performance and correctness in mind
Profile and improve existing codebases handling large analytical workloads
Build reusable numerical utilities to support actuarial analysis and diagnostics
Optimize computational bottlenecks in insurance reserving models
Collaborate closely with actuaries and subject-matter experts
Write clear, robust unit tests for numerical logic and edge cases
Implement domain-specific technical specifications using NumPy as the primary numerical tool
Translate written requirements and mathematical definitions into efficient, testable implementations
Apply vectorization and, where relevant, parallelism to process large data volumes
Ensure numerical results can be returned to calling services at speeds as close to real-time as possible
Actively participate in code reviews, providing and incorporating feedback with a focus on correctness, performance, and clarity
Work with relational databases (e.g. PostgreSQL) and/or NoSQL-style data
Confidently handle JSON-based data structures
Use Polars or Pandas for structured data manipulation and analysis
Translate between data representations as required (e.g. numerical matrix ↔ relational-style table ↔ dictionary / JSON)
Collaborate with peers to review data modeling and transformation approaches
Implement statistical models using Statsmodels for modeling and diagnostics (secondary focus)
Use Scikit-learn for ML workflow fundamentals (pipelines, metrics, validation — not core ML research)
Review and validate statistical and analytical implementations with peers to ensure correctness and maintainability
Implement features based on clear technical specifications and acceptance criteria
Write readable, maintainable code that supports effective peer review
Engage constructively in code reviews, both giving and receiving feedback
Ensure implementations are well-tested and aligned with agreed technical designs
Engineers manage branches with an appropriate level of complexity
Write clear commits and pull requests that are easy to review
Actively participate in peer code reviews
Work planned and delivered via well-defined stories
Engineers estimate work, understand story points, and proactively advance or close tickets
Ownership includes keeping ticket status up to date without reminders
Engineers contribute to and consume documentation as part of normal development

Benefits

Competitive salary + annual bonus
Health insurance , Dental and Vision coverage (including spouse and family coverage)
401K Company match
Life insurance
Cell Phone & Internet reimbursement
25 days of PTO/year
Commuter benefit
Gym membership via ClassPass
IT equipment allowance
Professional development & trainings
Team fun: regular company gatherings and team events