Mathematician Resume vs. Maersk Vacancy

Updated: March 26, 2026

Mathematician Resume vs. Maersk Vacancy

A detailed comparison of Lars Nielsen's resume against a vacancy at Maersk, highlighting strengths and areas for improvement.

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Overall score

43 /100

Weak matchLimited evidence

Evidence fit

ATS presentation

Confidence

60%

Lars Nielsen's Resume

Lars Nielsen

Mathematician

Email: larsnielsen@example.com

Phone: +45 12 34 56 78

Location: Copenhagen, Denmark

Summary

I am a mathematician with experience in a broad spectrum of fields such as software development, quantitative development and production planning and special interests in operations research, machine learning and any applications of mathematical modeling to real world problems. I am especially interested in applications of mathematical optimization to problems arising from energy systems. I am curious about applications of AI, and utilization of LLM to speed up software development. I am happy to dive into anything new and exciting, and work on problems that seem puzzling at first sight.

Experience

Research Associate

Sep 2021 - present

HUN-REN Institute for Computer Science and Control, remote

Published research results in several journals, including D1 and Q1 publications in the area of mathematics, computer science and manufacturing, and presented the results on multiple domestic and international conferences. Contributed to applied research projects in manufacturing systems and management to manage uncertainty, using machine learning, statistics and stochastic optimization techniques. Developed and implemented a vehicle routing and scheduling software for the logistic operations of a regional electrical installation company.

Software Developer

Feb 2021 - Sep 2021

HUN-REN Institute for Computer Science and Control, Budapest, HU

Developed and implemented a cutting-plane algorithm for an integrated system configuration and layout planning problem in a collaborative research project with Hitachi as industrial partner, resulting in 79% decrease in production line planning time.

Quantitative Developer, Strategist

Jan 2020 - Feb 2021

Morgan Stanley, Budapest, HU

Revalidated, stress tested and remediated several derivative pricing models used by the Commodity Trading Desk of the Fixed Income Division as part of the Regulatory Modeling team.

Software Developer

Oct 2018 - Jan 2020

Ericsson R&D, Budapest, HU

Developed and maintained the CI/CD and build system used by the Component Based Architecture unit as part of the Tools&Builds team. Integrated static/dynamic syntax validation tools into the automated build and code review loop and developed a blue-green deployment scheme for the Jenkins build server with Nginx.

Education

Eotvos Lorand University

Doctor of Philosophy, Mathematics and Computer Science Absolved; Thesis submission and defense expected in 2026

Budapest, HU

Eotvos Lorand University

Master of Science, Major: Applied Mathematics, Minor: Operations Research

Budapest, HU

Relevant Coursework: Markov Chains, Stochastic Processes; Machine Learning; Artificial Intelligence; Continuous Optimization, Integer Programming; Combinatorial Optimization; Numerical Methods for ODEs

Eotvos Lorand University

Bachelor of Science, Mathematics; Applied Mathematics Specialization

Budapest, HU

Relevant Coursework: Operations Research; C++Programming; Numerical Analysis, Probability Theory; Statistics; Data Science

Skills

Python, C++, SQL, Matlab, Git, Docker, Azure cloud, APIs, Xpress, Gurobi, Linux, Power BI, Excel, Cloud computing, CI/CD, Containerization

Languages

Hungarian: native

English: fluent

Norwegian: intermediate

Danish: beginner

Maersk Vacancy Description

Vacancy

Maersk

This role elevates our network design capability by building powerful simulation models that reveal how designs perform under real-world conditions.

Company

Maersk

Remote policy

flexible hybrid working

Responsibilities

Build simulation models that evaluate network design performance under real-world variability using data-driven and evidence-based planning concepts
Design and calibrate stochastic components to ensure simulation realism
Integrate internal and external data into simulation pipelines that reflect realistic operational behaviour
Analyze reported model deviations to quantify reliability, exposure to variability, systemic bottlenecks, and sensitivity to operational changes
Support the design of reliability metrics, simulation KPIs, and decision-support views
Validate simulation logic, assumptions, data inputs, and outputs through structured testing and statistical checks
Collaborate with internal teams to align models to external partners
Document assumptions, logic updates, data dependencies, and release notes in a clear and accessible format

Must-have requirements

Proficiency in Python for analytics, modelling, and building simulation components
Solid grounding in statistics, probability distributions, and stochastic modelling concepts
Familiarity with modern data science engineering practices including version control, reproducible modelling workflows, and cloud-based compute environments
Scientific, experimental mindset with the ability to test hypotheses quickly and iterate on modelling assumptions
Understanding of network operations, variability drivers, and supply chain processes
Ability to analyse complex logic, validate models, and debug data/logic behaviour
Clear communication of modelling logic, choices, data dependencies, and results
Collaborative mindset and ability to operate across planning, tech, and business functions

Benefits

world-class learning
supportive leaders
flexible hybrid working
industry-leading benefits

Tech stack

Python

Resume Fit Analysis

Warnings

The resume lacks explicit evidence for several must-have requirements, particularly in network operations and supply chain processes.
The summary does not strongly align with the vacancy's focus on network design and simulation models.
The resume does not provide clear evidence of the candidate's ability to communicate modelling logic and results.

Section breakdown

Summary

10/22Counted

The summary mentions mathematical optimization and machine learning but lacks focus on network design and simulation models, which are critical for the vacancy.

Supporting evidence

Experience in operations research and machine learning.

Missing evidence

Network design and simulation models.

Improvement hint

Enhance the summary to explicitly mention experience with network design and simulation models.

Skills and ATS

12/18Counted

Skills section includes Python and cloud computing, but lacks explicit mention of data science engineering practices and network operations.

Supporting evidence

Python
Cloud computing

Missing evidence

Data science engineering practices
Network operations

Improvement hint

Include skills related to data science engineering practices and network operations.

Experience

23/28Counted

Experience in software development and quantitative analysis is relevant, but lacks direct evidence of network operations and supply chain processes.

Supporting evidence

Developed and implemented a vehicle routing and scheduling software.
Revalidated and stress tested derivative pricing models.

Missing evidence

Network operations
Supply chain processes

Improvement hint

Highlight any experience related to network operations and supply chain processes.

Education

ExcludedExcluded

Education is not explicitly required by the vacancy.

Language and location

ExcludedExcluded

Language and location are not explicitly required by the vacancy.

Contact readiness

7/7Counted

Both email and phone are present, providing strong contact readiness.

Supporting evidence

Email: larsnielsen@example.com
Phone: +45 12 34 56 78

Improvement hint

Ensure contact details remain up-to-date.

Nice-to-have bonus

ExcludedExcluded

No nice-to-have requirements specified in the vacancy.

Strengths

Proficiency in Python and experience with stochastic optimization.
Experience in model validation and debugging.

Risks

Lack of explicit evidence for network operations and supply chain processes.
Summary does not strongly align with the vacancy's focus on network design and simulation models.

Must-have requirements

Proficiency in Python for analytics, modelling, and building simulation components

Exact

Score impact: 100/100

Python is explicitly listed in the skills section and supported by job descriptions.

Python

Solid grounding in statistics, probability distributions, and stochastic modelling concepts

Strong

Score impact: 90/100

The resume mentions stochastic optimization and relevant coursework in statistics.

Stochastic optimization techniques
Relevant coursework: Statistics

Familiarity with modern data science engineering practices including version control, reproducible modelling workflows, and cloud-based compute environments

Weak

Score impact: 80/100

Skills section lists Git and Azure cloud, but lacks detail on reproducible workflows.

Git
Azure cloud

Scientific, experimental mindset with the ability to test hypotheses quickly and iterate on modelling assumptions

Weak

Score impact: 70/100

The resume suggests a scientific mindset but lacks explicit evidence of hypothesis testing.

Curious about applications of AI
Contributed to applied research projects

Understanding of network operations, variability drivers, and supply chain processes

None

Score impact: 100/100

No explicit evidence of network operations or supply chain processes.

Ability to analyse complex logic, validate models, and debug data/logic behaviour

Strong

Score impact: 90/100

Experience in model validation and debugging is evident in job descriptions.

Revalidated, stress tested and remediated several derivative pricing models

Clear communication of modelling logic, choices, data dependencies, and results

Weak

Score impact: 80/100

Presentation experience is mentioned but lacks detail on communication of modelling logic.

Presented the results on multiple domestic and international conferences

Collaborative mindset and ability to operate across planning, tech, and business functions

Strong

Score impact: 85/100

The resume indicates collaboration in research projects.

Collaborative research project with Hitachi

Recommendations

Revise the summary to explicitly mention experience with network design and simulation models.

High

Section: summaries

Score impact: +10

The summary should align more closely with the vacancy's focus to improve ATS and recruiter visibility.

Add skills related to data science engineering practices and network operations.

Medium

Section: skills

Score impact: +8

Enhancing the skills section will improve ATS matching and evidence coverage.

Scoring notes

The analysis is conservative due to limited explicit evidence for some must-have requirements.