Resume Analysis for Data Scientist - Applied AI & Optimisation at DFDS

Updated: March 26, 2026

Resume Analysis for Data Scientist - Applied AI & Optimisation at DFDS

An analysis of Lars Nielsen's resume for the Data Scientist position at DFDS, focusing on applied AI and optimisation.

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Overall score

50 /100

Weak matchLimited evidence

Evidence fit

ATS presentation

Confidence

70%

Lars Nielsen's Resume

Lars Nielsen

Copenhagen, Denmark | +45 12 34 56 78 | larsnielsen@example.com

Experience

Research Associate

Sep 2021 - present

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 to manage uncertainty, using machine learning, statistics and stochastic optimization techniques. Developed and implemented a vehicle routing and scheduling software for a regional electrical installation company.

Software Developer

Feb 2021 - Sep 2021

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

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

Developed and maintained the 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, Xpress, Gurobi, Linux, Power BI, Excel

Languages

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

Data Scientist - Applied AI & Optimisation at DFDS

Vacancy

Data Scientist - Applied AI & Optimisation · DFDS

Help build AI systems that power real-world logistics at DFDS, focusing on applied AI and optimisation in logistics, ferry, and terminal operations.

Role

Data Scientist - Applied AI & Optimisation

Company

DFDS

Location

Copenhagen Municipality, Capital Region of Denmark, Denmark

Employment type

Full-time

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.

Benefits

Modern workspace with ocean views.
Easy access to public transport.
Strong social and community culture.
Employee sports clubs, choir, and other shared activities.
On-site facilities for connecting with colleagues.

Tech stack

Python

APIs

cloud

containers

Fit Analysis for Data Scientist Role

Warnings

The resume lacks a summary section, which limits ATS visibility and early recruiter engagement.
The vacancy requires experience with ML/statistical models in production, which is not explicitly evidenced in recent jobs.
The resume does not explicitly mention experience with APIs or cloud technologies, which are part of the tech stack.

Section breakdown

Summary

0/22Counted

The resume lacks a summary section, which limits the visibility of key skills and experiences to ATS and recruiters.

Missing evidence

Summary section

Improvement hint

Add a summary that highlights key skills and experiences relevant to the vacancy.

Skills and ATS

10/18Counted

The skills section includes Python and C++, which are relevant, but lacks explicit mention of APIs and cloud technologies.

Supporting evidence

Python
C++

Missing evidence

APIs
cloud technologies

Improvement hint

Include skills related to APIs and cloud technologies to improve ATS matching.

Experience

20/28Counted

The candidate has relevant experience in software development and optimization, but lacks explicit evidence of ML/statistical models in production environments.

Supporting evidence

Developed and implemented a vehicle routing and scheduling software
Developed and implemented a cutting-plane algorithm

Missing evidence

Experience with ML/statistical models in production

Improvement hint

Highlight specific projects involving ML/statistical models in production environments.

Education

10/10Counted

The candidate holds a Master's degree in Applied Mathematics, which aligns well with the educational requirements.

Supporting evidence

Master of Science in Applied Mathematics

Improvement hint

No improvement needed in education section.

Language and location

ExcludedExcluded

Language and location are not explicitly required by the vacancy.

Contact readiness

6/7Counted

The resume includes both email and phone, but lacks a clear target role in the personal details.

Supporting evidence

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

Missing evidence

Clear target role

Improvement hint

Specify a target role in the personal details to enhance contact readiness.

Nice-to-have bonus

0/5Counted

The resume does not mention curiosity about LLMs or modern AI, which are nice-to-have requirements.

Missing evidence

Curiosity about LLMs and modern AI

Improvement hint

Mention any interest or experience with LLMs or modern AI to gain a bonus.

Strengths

Strong educational background with a Master's degree in Applied Mathematics.
Experience in mathematical modelling and optimization.

Risks

Lack of explicit evidence for ML/statistical models in production environments.
Missing summary section reduces ATS and recruiter engagement.

Critical issues

The resume lacks a summary section, which limits ATS visibility and early recruiter engagement.

Must-have requirements

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

Exact

Score impact: 100/100

The candidate's Master's degree in Applied Mathematics meets the educational requirement.

Master of Science in Applied Mathematics

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

Weak

Score impact: 100/100

The resume mentions machine learning and statistics, but lacks explicit evidence of production experience.

Contributed to applied research projects in manufacturing to manage uncertainty, using machine learning, statistics and stochastic optimization techniques.

Strong skills in modelling, mathematics, and problem formulation.

Strong

Score impact: 100/100

The candidate has demonstrated skills in mathematical modelling and problem-solving.

Developed and implemented a cutting-plane algorithm
Developed and implemented a vehicle routing and scheduling software

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

Weak

Score impact: 100/100

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

Developed and implemented a vehicle routing and scheduling software

Nice-to-have requirements

Curiosity about LLMs and modern AI.

None

Score impact: 0/100

The resume does not mention LLMs or modern AI.

Recommendations

Add a summary section that highlights key skills and experiences relevant to the vacancy.

High

Section: summaries

Score impact: +15

A well-crafted summary can improve ATS visibility and provide recruiters with a quick overview of the candidate's fit for the role.

Include skills related to APIs and cloud technologies.

Medium

Section: skills

Score impact: +10

These skills are part of the tech stack and their inclusion can improve ATS matching.

Scoring notes

The analysis is conservative due to the lack of a summary section and limited explicit evidence of ML/statistical models in production.