Mathematician CV vs. Data Scientist Vacancy at DFDS

Updated: April 4, 2026

Mathematician CV vs. Data Scientist Vacancy at DFDS

Explore how a mathematician's CV aligns with a Data Scientist position at DFDS, focusing on AI and optimization skills.

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Overall score

52 /100

Moderate matchLimited evidence

Evidence fit

ATS presentation

Confidence

70%

Mathematician 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 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

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 a the logistic operations of 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

Doctor of Philosophy

Sep 2021 - present

Eotvos Lorand University, Budapest, HU

Mathematics and Computer Science Absolved; Thesis submission and defense expected in 2026

Master of Science

Sep 2018 - Jan 2021

Eotvos Lorand University, Budapest, HU

Major: Applied Mathematics, Minor: Operations Research Relevant Coursework: Markov Chains, Stochastic Processes; Machine Learning; Artificial Intelligence; Continuous Optimization, Integer Programming; Combinatorial Optimization; Numerical Methods for ODEs

Bachelor of Science

Sep 2015 - Jun 2018

Eotvos Lorand University, Budapest, HU

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

Skills

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

Languages

Hungarian (native), English (fluent), Norwegian (intermediate), Danish (beginner)

Data Scientist - Applied AI & Optimisation

Vacancy

Data Scientist - Applied AI & Optimisation · DFDS

Help build AI systems that power real-world logistics at DFDS, focusing on Applied AI and optimisation.

Role

Data Scientist - Applied AI & Optimisation

Company

DFDS

Location

Copenhagen Municipality, Capital Region of Denmark, Denmark

Employment type

Full-time

Seniority

early- to mid-career

Responsibilities

Translate operational problems into analytical and modelling approaches.
Develop ML models, statistical methods, and optimisation solutions.
Design and build AI products leveraging agentic workflows.
Build MVPs and iterate them into production with engineers.
Collaborate with stakeholders and communicate results clearly.

Must-have requirements

M.Sc. or Ph.D. in Computer Science, Mathematics, Physics, Engineering, or similar.
~1–5 years of experience working with ML/statistical models.
Strong skills in modelling, mathematics, and problem formulation.
Experience with designing, training and implementing predictive models or optimisation algorithms in a production environment.

Nice-to-have requirements

Curiosity about LLMs and modern AI.
Ability to prototype solutions in Python and collaborate with engineers on production.

Benefits

Modern workspace with ocean views and easy access to public transport.
Strong social and community culture, including employee sports clubs and choir.
On-site facilities for connecting with colleagues.
Focus on real-world impact, learning, and experimentation.
Continuous development through T&I’s career development model, GROW.

Tech stack

Python

APIs

cloud

containers

Fit Analysis for Data Scientist Role

Warnings

The resume lacks explicit evidence for some must-have requirements, particularly in the summary and skills sections.
The vacancy requires experience with ML/statistical models in production, which is not clearly evidenced in recent roles.

Section breakdown

Summary

12/22Counted

The summary partially aligns with the vacancy, mentioning mathematical modeling and optimization but lacks explicit mention of AI or ML models.

Supporting evidence

Experience in operations research and mathematical optimization.

Missing evidence

Explicit mention of AI or ML models.

Improvement hint

Include specific references to AI and ML experience in the summary.

Skills and ATS

9/18Counted

Skills section includes Python and cloud computing but lacks explicit mention of APIs and containers, which are part of the tech stack.

Supporting evidence

Python
Cloud computing

Missing evidence

APIs
Containers

Improvement hint

Add skills related to APIs and containerization to strengthen ATS alignment.

Experience

20/28Counted

Experience in quantitative development and software development is relevant, but recent roles do not clearly evidence ML model deployment in production.

Supporting evidence

Developed and implemented optimization algorithms.
Experience with stochastic optimization techniques.

Missing evidence

Direct experience with ML model deployment in production.

Improvement hint

Highlight any experience with deploying ML models in production environments.

Education

10/10Counted

The candidate holds a Ph.D. in Mathematics, which aligns perfectly with the educational requirement.

Supporting evidence

Ph.D. in Mathematics from Eotvos Lorand University.

Improvement hint

No improvement needed.

Language and location

ExcludedExcluded

Language and location are not explicitly required by the vacancy.

Contact readiness

7/7Counted

Both email and phone are present, with clear location context in Denmark.

Supporting evidence

Email: larsnielsen@example.com
Phone: +45 12 34 56 78
Location: Copenhagen, Denmark

Improvement hint

No improvement needed.

Nice-to-have bonus

2/5Counted

The resume shows some curiosity about AI, but lacks explicit mention of LLMs.

Supporting evidence

Interest in machine learning and mathematical modeling.

Missing evidence

Curiosity about LLMs.

Improvement hint

Mention any specific interest or experience with LLMs.

Strengths

Strong educational background with a Ph.D. in Mathematics.
Experience in mathematical modeling and optimization.

Risks

Lack of explicit evidence for ML model deployment in production environments.
Summary and skills sections do not fully align with AI and ML requirements.

Must-have requirements

M.Sc. or Ph.D. in Computer Science, Mathematics, Physics, Engineering, or similar.

Exact

Score impact: 100/100

The candidate holds a Ph.D. in Mathematics, which meets the educational requirement.

Ph.D. in Mathematics from Eotvos Lorand University.

~1–5 years of experience working with ML/statistical models.

Weak

Score impact: 80/100

The resume mentions experience with optimization techniques, but lacks clear evidence of ML model deployment in production.

Experience with stochastic optimization techniques.

Strong skills in modelling, mathematics, and problem formulation.

Strong

Score impact: 90/100

The candidate has strong skills in mathematical modeling and optimization.

Experience in operations research and mathematical optimization.

Experience with designing, training and implementing predictive models or optimisation algorithms in a production environment.

Weak

Score impact: 85/100

The resume shows experience with optimization algorithms but lacks explicit mention of predictive models in production.

Developed and implemented optimization algorithms.

Nice-to-have requirements

Curiosity about LLMs and modern AI.

Weak

Score impact: 40/100

The resume indicates interest in machine learning but does not explicitly mention LLMs.

Interest in machine learning and mathematical modeling.

Ability to prototype solutions in Python and collaborate with engineers on production.

Strong

Score impact: 60/100

The candidate has strong Python skills and experience in developing algorithms.

Python
Developed and implemented optimization algorithms.

Recommendations

Include specific references to AI and ML experience in the summary.

High

Section: summaries

Score impact: +10

To improve alignment with the vacancy's focus on AI and ML.

Add skills related to APIs and containerization.

Medium

Section: skills

Score impact: +5

To better match the tech stack requirements of the vacancy.

Scoring notes

The resume provides strong evidence of mathematical and optimization skills but lacks explicit mention of AI and ML experience in production, which is critical for the vacancy.