Professional Summary
Technical Project Manager with over five years of experience leading cross-functional teams through the delivery of high-stakes AI and data initiatives. I specialize in bridging the gap between engineering and business, turning complex technical hurdles into clear, actionable roadmaps. My focus is on driving measurable outcomes through Agile delivery and proactive risk management, ensuring we ship on time without losing team momentum.
Projects
BTFD ↗
Making market timing cool again
GoAuksjon
GoAuksjon is a modern, responsive hub for the Norwegian market that replaces clunky lists with interactive charts and lightning-fast global search. Clean design, smarter filters, and zero friction.
Mapillary City Explorer
Navigate streets of any city with Mapillary
Semantic Document Grouping ↗
Soda Time
Soda Time is a React version of Popcorn-Time built to provide synopsis for your favourite movies
Professional Experience
May 2025 – Present
Senior Data Scientist
Crayon Consulting · Full-time
Oslo, Norway · On-site
Research and Development (R&D)Jul 2023 – May 2025
non-exec Artificial Intelligence Director
Cosmic Wire Inc · Contract
- Advised on AI strategy by balancing "big picture" commercial goals with what is actually achievable from a technical perspective.
- Worked across departments to ensure the technical vision stayed in sync with product and commercial needs.
- Defined the priorities and reporting structures that kept AI projects organized and accountable.
- Identified and mitigated delivery risks (data availability, vendor lock-in, infra constraints) early in the lifecycle, preventing costly overruns on key AI projects.
Cross-functional CollaborationsStakeholder ManagementAgile MethodologiesProject ManagementRisk ManagementLeadershipStrategic RoadmapsKPI ReportingProject GovernanceIT StrategyMar 2022 – May 2025
Senior Data Scientist
Aize · Full-time
Oslo, Norway · On-site
- Led end-to-end delivery of data and computer vision initiatives, steering internal teams and client stakeholders from the initial discovery phase through to final delivery.
- Managed the intersection of data science, computer vision, and platform engineering, ensuring cross-functional teams stayed aligned with enterprise client needs.
- Successfully balanced 8+ concurrent enterprise accounts, syncing with Sales, Product, and Customer Success to keep technical milestones on track and meet every deadline.
- Served as the "technical translator," breaking down complex ML/CV challenges into clear, business-focused updates for non-technical leadership.
- Collaborative partner in portfolio management, helping leadership prioritize high-impact work and optimize team capacity across various engagements.
MentoringAgile MethodologiesCross-functional CollaborationsComputer GraphicsPortfolio ManagementCapacity ManagementKPI DashboardsSep 2015 – Oct 2023
Academic Lecturer & Technical Instructor
Kristiania University College, University of Edinburgh, Heriot-Watt University · Contract
Various Academic Institutions in Oslo & Edinburgh
(Oct 2021 - Oct 2023) Kristiania University College
Designed and delivered curriculum for Android Software Engineering with Kotlin, managing the learning roadmap for 100+ students to ensure core competency attainment.
(Jun 2017 - Jul 2017) University of Edinburgh through LEAPS programme
Teaching Android programming to pre-university teenagers
(Sep 2015 - Oct 2021) Heriot-Watt University and University of Edinburgh
Provided technical mentorship and code reviews for student projects, fostering best practices in software engineering and version control
TeachingUniversity TeachingMentoringJan 2020 – Jun 2022
Machine Learning Lead
Buyaladdin.com, Inc · Contract
- Managed the ML roadmap for an e-commerce platform, aligning technical priorities with product and commercial objectives.
- Built the ML function from the ground up, acting as the bridge between engineering, product, and business stakeholders to get features shipped.
- Modernized development workflow by introducing CI/CD and automated code reviews. This made our entire production cycle more reliable and less prone to manual errors.
- Led a team of 2 engineers, implementing quarterly planning and sprint structures to deliver all major milestones on schedule.
- Owned the redesign of our core recommendation engine. Beyond the tech, I focused on setting the engineering standards and code review.
Image ProcessingStakeholder ManagementAgile MethodologiesRisk ManagementCross-functional CollaborationsLeadershipAmazon Web Services (AWS)Project ManagementScrumRelease ManagementSoftware Development Life Cycle (SDLC)Cross-functional Team LeadershipApr 2021 – Mar 2022
Research Scientist
SINTEF Digital · Full-time
Oslo, Norway · On-site
- Led client-facing R&D and PoC projects, taking full ownership of the lifecycle - from initial scoping and timeline management to final stakeholder handoff.
- Advised enterprise clients, bridging the gap between "big picture" business goals and technical feasibility to build actionable product roadmaps.
- Delivered projects on time by proactively managing resource needs and controlling scope changes.
- Collaborated across specialized teams, acting as the "glue" between internal researchers, client engineers, and product leadership to resolve technical blockers.
- Served as a gatekeeper for technical risk; my feasibility studies prevented clients from sinking capital into unviable tech, instead pivoting them toward more reliable, high-growth solutions.
Stakeholder ManagementImage ProcessingResearchScope ManagementFeasibility StudiesResearch and Development (R&D)Technology TransferCustomer EngagementNov 2020 – Feb 2022
Computer Vision Engineer
ViaTech AS · Contract
- Tech Lead for a production CV pipeline, overseeing the architecture and working across hardware and product teams to bring the system to life.
- Built the mobile data processing infrastructure from the ground up, ensuring the technical design aligned with both hardware limits and product goals.
- Streamlined how we handle point clouds, imagery, and laser scans - refining our sync and post-processing workflows to make them faster and more precise.
- Acted as the technical glue between hardware engineering and product stakeholders to keep feature requirements realistic and on track.
- Mentored junior talent in computer vision and production standards, helping a direct report transition into an expanded, high-impact role.
- Owned the technical roadmap, making the tough calls on when to prioritize new algorithms versus fixing technical debt.
Stakeholder ManagementAgile MethodologiesCross-functional CollaborationsImage ProcessingComputer VisionProject ManagementDebt ManagementService-Level Agreements (SLA)System ArchitectureMentoringSep 2017 – Sep 2018
Visiting Researcher & R&D Intern
Adobe & Volkswagen · Contract
(Jun 2018 – Sep 2018) Adobe
Engineered a novel machine learning solution for risk assessment, translating abstract theoretical concepts from upper management into a deployable software artifact. Workflow was showcased at Adobe Summit
(Sep 2017 – Oct 2017) Volkswagen Data:Lab
Collaborated in a hackathon-style environment, demonstrating the ability to deliver complex technical MVPs with limited resources
Project ManagementRequirements ManagementCross-functional CollaborationsNov 2010 – Aug 2015
Early Career Engineering
Infront-X, DEFA, SKM, Petrell · Full-time
Trondheim, Trøndelag, Norway · On-site
Held various software engineering roles delivering robust solutions for financial, automotive, and energy sectors. Focus on Agile delivery, Android/Mobile development, Delphi, and C++ systems
(Jan 2013 – Aug 2015) Infront-X
Responsible for a portfolio of 3 customer-facing products. Modernized legacy Delphi codebases, improving system maintainability and performance. Improved internal processes for bug tracking and fixes.
(May 2012 – Jan 2013) DEFA
Product owner for an Android mobile application that enabled remote control features for automotive heating and alarm systems.
(May 2011 – Oct 2011) SKM
Delivered Android-based mobile solution, ensuring cross-device compatibility and user-friendly interface design
(Nov 2010 – Feb 2011) Petrell
Conducted rigorous code analysis and debugging for C++ / Qt applications, identifying and resolving critical stability issues in legacy software.
Stakeholder ManagementScrumAgile Project Management
Education
- 2015 - 2020
The University of Edinburgh
Doctor of Philosophy (Ph.D.) • Robotics and Autonomous Systems
- 2014 - 2015
Heriot-Watt University
Master’s Degree • Software Engineering
- 2007 - 2012
Norwegian University of Science and Technology (NTNU)
Bachelor's degree • Computer Science
Certifications & Credentials
Intermediate Threat Modeling
SecureFlag · Issued Oct 2025 · Expires Oct 2026
DNV Green Belt (Mandatory)
SecureFlag · Issued Oct 2025 · Expires Oct 2026
DNV White Belt (Mandatory)
SecureFlag · Issued Oct 2025 · Expires Oct 2026
Volunteering
Board Member
Tekna
Nov 2022 - Present • 3 yrs 5 mos • Big Data
Office Bearer
Robotics Society at Heriot-Watt University
Jun 2017 - Jan 2019 • 1 yr 8 mos • Education
Student society
Accounting volunteer
SHRUB: Co-op
Aug 2018 - Dec 2018 • 5 mos • Environment
Demonstrator
Heriot-Watt University Year of Robotics
Jan 2017 - Dec 2017 • 1 yr • Education
Promoting Robotics across UK by participating in festivals and demonstrating equipment in schools
2) hw.ac.uk ↗
3) hw.ac.uk ↗
Workshop Lead
Scottish Informatics and Computer Science Alliance (SICSA)
Feb 2017 - Oct 2017 • 9 mos • Education
Workshop "Ethical, Social, and Professional Issues in Computer Science" organiser
Local Committee President
AIESEC
Aug 2011 - Nov 2011 • 4 mos
Publications
- Poster: Point Cloud Data Simulation and Modelling with Aize Workspace ↗
Jan 9, 2023
This work takes a look at data models often used in digital twins and presents preliminary results specifically from surface reconstruction and semantic segmentation models trained using simulated data. This work is expected to serve as a ground work for future endeavours in data contextualisation inside a digital twin.
- Towards Sustainable Smart Cities: The Use of the ViaPPS as Road Monitoring System ↗
Springer Nature · Nov 3, 2022
Smart cities are an opportunity to overcome the concerns regarding the rapid increase in highly dense populated urban areas. Using ICT, smart cities make urban areas greener, sustainable and at the same time increase their competitiveness and their economic growth. Road networks play a significant role to improve the sustainability of smart cities. Indeed, deteriorated roads cause tied mobility, traffic congestion, CO2 emissions and economic damages to cities and their citizens. To prevent these negative aspects, the road network have to be continuously maintained. A satisfactory road maintenance relies on a continuous monitoring of road network which can be facilitated deploying ICT. This article presents the role of ICT in road monitoring and showcases the ViaPPS: a mobile Pavement Profiling System. The ViaPPS offers accurate and detailed geo-referenced information of the state of the road and the corresponding furniture with the deployment of LiDAR and computer vision techniques. After presenting the capabilities of the ViaPPS, this article discusses the strategy of the smart city of Oslo towards its sustainable development goals and the role of ICT in road maintenance.
- ViaPPS: A Mobile Pavement Profiling System ↗
Jan 1, 2021
Ensuring safety levels on roads is an imperative task for road authorities. Significant amounts of time and money are spent on performing road inspections every year. In order to ensure efficiency of road inventories, road practitioners are in need of reliable systems that are fast and are designed to comply with high standards at lower costs. To date, the 3D Mobile Mapping Systems is the most efficient, productive and accurate system used during evaluation of pavement inventories. This paper presents ViaPPS, a 3D Mobile Mapping System from ViaTech AS. ViaPPS offers accurate georeferenced data by combining the perception and navigation sensors. This paper showcases results from multiple mobile systems after the harmonization process which is held once a year to evaluate reliability and repeatability of the measurements.
- Transfer Learning for British Sign Language Modelling ↗
Association for Computational Linguistics · Aug 1, 2018
Automatic speech recognition and spoken dialogue systems have made great advances through the use of deep machine learning methods. This is partly due to greater computing power but also through the large amount of data available in common languages, such as English. Conversely, research in minority languages, including sign languages, is hampered by the severe lack of data. This has led to work on transfer learning methods, whereby a model developed for one language is reused as the starting point for a model on a second language, which is less resourced. In this paper, we examine two transfer learning techniques of fine-tuning and layer substitution for language modelling of British Sign Language. Our results show improvement in perplexity when using transfer learning with standard stacked LSTM models, trained initially using a large corpus for standard English from the Penn Treebank corpus.
- Classification of Phonological Parameters in Sign Languages ↗
Signers compose sign language phonemes that enable communication by combining phonological parameters such as handshape, orientation, location, movement, and non-manual features. Linguistic research often breaks down signs into their constituent parts to study sign languages and often a lot of effort is invested into the annotation of the videos. In this work we show how a single model can be used to recognise the individual phonological parameters within sign languages with the aim of either to assist linguistic annotations or to describe the signs for the sign recognition models. We use Danish Sign Language data set `Ordbog over Dansk Tegnsprog' to generate multiple data sets using pose estimation model, which are then used for training the multi-label Fast R-CNN model to support multi-label modelling. Moreover, we show that there is a significant co-dependence between the orientation and location phonological parameters in the generated data and we incorporate this co-dependence in the model to achieve better performance.
- Towards Large-Scale Data Mining for Data-Driven Analysis of Sign Languages ↗
Access to sign language data is far from adequate. We show that it is possible to collect the data from social networking services such as TikTok, Instagram, and YouTube by applying data filtering to enforce quality standards and by discovering patterns in the filtered data, making it easier to analyse and model. Using our data collection pipeline, we collect and examine the interpretation of songs in both the American Sign Language (ASL) and the Brazilian Sign Language (Libras). We explore their differences and similarities by looking at the co-dependence of the orientation and location phonological parameters
Honors & Awards
Winner of I’m an Engineer competion in the Artificial Body Zone category
Jun 2018
James Watt Scholarship
Feb 2015
Energetic ideas for the future
Equinor · Aug 2010
A comprehensive 30-page essay on four ideas to generate renewable power by 2050. equinor.com ↗
Languages
- Bokmål, Norwegian
- English
- Lithuanian
- Russian
Patents
- Methods and systems for facilitating authenticating of users ↗
Patent Number: US20230195862A1 • Issued Jul 23, 2023
Method of facilitating authenticating of users. The method includes initiating, using a processing device, an authentication session for a user for an authentication instance.Method of facilitating authenticating of users. The method includes initiating, using a processing device, an authentication session for a user for an authentication instance.
- Vertex interpolation in one-shot learning for object classification ↗
Patent Number: US 20220147766A1 • Issued May 12, 2022
Synthesising training data by augmenting objects of interest in images with the help of vertex interpolation on an overlayed object-dependent smart grid