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
HUN-REN Institute for Computer Science and Control
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 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
HUN-REN Institute for Computer Science and Control
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
Morgan Stanley
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
Ericsson R&D
Oct 2018 - Jan 2020
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
Eotvos Lorand University
Sep 2021 - present
Mathematics and Computer Science Absolved; Thesis submission and defense expected in 2026
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
Responsibilities
Develop and deliver AI solutions for logistics, ferry, and terminal operations.
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
Curious about LLMs and modern AI.
Prototype solutions in Python and collaborate with engineers on production.
Benefits
Modern workspace with ocean views.
Easy access to public transport.
Employee sports clubs, choir, and other shared activities.
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 experience with predictive models or optimisation algorithms in a production environment.
Section breakdown
Summary
15/22Counted
The summary reflects a good alignment with the vacancy, mentioning experience in mathematical modeling, machine learning, and optimization, which are relevant to the role.
Supporting evidence
Experience in operations research, machine learning, and mathematical optimization.
Missing evidence
Specific mention of AI solutions for logistics.
Improvement hint
Include more specific references to AI applications in logistics and production environments.
Skills and ATS
13/18Counted
The skills section covers many relevant technologies such as Python, APIs, and cloud computing, but lacks explicit mention of containers.
Supporting evidence
Python (numpy, pandas, scipy)
APIs
Cloud computing
Missing evidence
Explicit mention of container technologies.
Improvement hint
Add explicit mention of container technologies like Docker in the skills section.
Experience
18/28Counted
The experience section shows relevant roles in software development and quantitative analysis, but lacks explicit evidence of production-level AI solutions.
Supporting evidence
Developed and implemented a vehicle routing and scheduling software.
Revalidated, stress tested and remediated several derivative pricing models.
Missing evidence
Direct experience with AI solutions in logistics.
Improvement hint
Highlight any experience with AI solutions in logistics or production environments.
Education
10/10Counted
The candidate holds a Ph.D. in Mathematics and Computer Science, which aligns perfectly with the educational requirements.
Supporting evidence
Ph.D. in Mathematics and Computer Science from Eotvos Lorand University.
Improvement hint
No improvement needed.
Language and location
ExcludedExcluded
Language and location are not explicitly required by the vacancy.
Improvement hint
No improvement needed.
Contact readiness
6/7Counted
Both email and phone are present, with a clear target role and location.
Supporting evidence
Email: larsnielsen@example.com
Phone: +45 12 34 56 78
Improvement hint
Ensure all contact details are up-to-date and clearly visible.
Nice-to-have bonus
3/5Counted
The candidate shows curiosity about AI and LLMs, which aligns with the nice-to-have requirements.
Supporting evidence
Curiosity about applications of AI and LLM.
Missing evidence
Explicit collaboration with engineers on production.
Improvement hint
Provide examples of collaboration with engineers on production solutions.
Strengths
Strong educational background with a Ph.D. in Mathematics and Computer Science.
Relevant experience in mathematical modeling and optimization.
Risks
Lack of explicit evidence for production-level AI solutions in logistics.
Must-have requirements
M.Sc. or Ph.D. in Computer Science, Mathematics, Physics, Engineering, or similar.
Exact
Score impact: 100/100
The candidate's educational background matches the requirement exactly.
Ph.D. in Mathematics and Computer Science from Eotvos Lorand University.
1–5 years of experience working with ML/statistical models.
Strong
Score impact: 100/100
The candidate has relevant experience in ML and statistical models.
Experience in machine learning and stochastic optimization techniques.
Revalidated, stress tested and remediated several derivative pricing models.
Strong skills in modelling, mathematics, and problem formulation.
Exact
Score impact: 100/100
The candidate's skills and experience align well with this requirement.
Experience in mathematical modeling and optimization.
Developed and implemented a cutting-plane algorithm.
Experience with designing, training and implementing predictive models or optimisation algorithms in a production environment.
Weak
Score impact: 100/100
The resume shows some relevant experience but lacks explicit production-level evidence.
Developed and implemented a vehicle routing and scheduling software.
Nice-to-have requirements
Curious about LLMs and modern AI.
Exact
Score impact: 50/100
The candidate explicitly mentions curiosity about AI and LLMs.
Curious about applications of AI, and utilization of LLM to speed up software development.
Prototype solutions in Python and collaborate with engineers on production.
Weak
Score impact: 50/100
The candidate has Python skills but lacks explicit evidence of collaboration with engineers on production.
Python (numpy, pandas, scipy)
Recommendations
Include specific examples of AI solutions developed for logistics or production environments.
High
Section: summaries
Score impact: +10
This will strengthen the alignment with the vacancy's core requirements.
Add explicit mention of container technologies like Docker.
Medium
Section: skills
Score impact: +5
This will improve ATS visibility for relevant technologies.
Scoring notes
The analysis is conservative due to limited explicit evidence for some must-have requirements.
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