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Python Numerical Computing Engineer

Job in Atlanta, Cass County, Texas, 75551, USA
Listing for: Akur8
Full Time position
Listed on 2026-02-24
Job specializations:
  • Software Development
    Data Scientist, AI Engineer
Salary/Wage Range or Industry Benchmark: 90000 - 120000 USD Yearly USD 90000.00 120000.00 YEAR
Job Description & How to Apply Below

Akur8
is a fast-growing Insur Tech 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 Insur Tech Top 150 (2025)

  • Insur Tech
    100 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.

WHAT YOU’LL DO
  • 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

CORE TECHNICAL FOCUS
NUMERICAL COMPUTING
  • Implement domain-specific technical specifications using Num Py 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

DATA & STORAGE
  • Work with relational databases (e.g. Postgre

    SQL) and/or No

    SQL-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

STATISTICAL & REPORTING TOOLS (Supporting Role)
  • Implement statistical models using Stats models 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

ENGINEERING PRACTICES (CROSS-CUTTING)
  • 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…

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