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. 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 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

DATA & STORAGE

• 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

STATISTICAL & REPORTING TOOLS (Supporting Role)

• 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

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 receiving feedback
• Ensure implementations are well-tested and aligned with agreed technical designs

TECHNICAL ENVIRONMENT & ENGINEERING WORKFLOW TECHNICAL ENVIRONMENT

• Python (core language)
• NumPy, Polars / Pandas for numerical computing and data manipulation
• Statsmodels, Scikit-learn (supporting analytical tooling)
• PostgreSQL, JSON-based data storage
• AWS
• Wider company stack includes C#, .NET, Angular, Kubernetes

ENGINEERING WORKFLOW

Candidates must be team players with strong interpersonal skills, as well as:

• 3+ years of experience using Python for numerical computing
• Proven experience in writing production quality Python code

BONUS skills (experience you may not have, but which will make you stand out if you do)

• 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

As a newcomer, you'll be joining a diverse, highly skilled and motivated team, with a strong Tech DNA, colleagues that are eager to share their knowledge and passion. But it’s not all work, you’ll also be part of a dynamic team that enjoys spending time together and having fun, including karaoke, team lunches, playing sports as well as the occasional ‘happy hour’.

In Addition To This, We Will Provide You With

• 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