I’m Misha. My econometrics master’s thesis explored Formula 1 race simulations. I now study AI and want to put that combination to work at Red Bull Powertrains.
DATA STRATEGY & INSIGHTS | STUDENT PLACEMENT | 2027
MISHA MARKOVITSECONOMETRICS | AI | FORMULA 1Currently in Melbourne. AI exchange at Deakin University.
F1 RACE SIMULATIONDATA ENGINEERINGMACHINE LEARNINGTEAM LEADERSHIP
THE MISHA STORY LAP
One lap. My story.
Follow the racing line through my research, the teams I’ve worked with and the sport that started it all.
Scroll to drive. Pick a checkpoint to jump ahead.
A circuit of my story, inspired by Formula 1.
MISHA / STORY CIRCUIT1 LAP / 6 CHECKPOINTS
YOUR SCROLL IS THE THROTTLE↓ READ THE NEXT CORNER
S1 / RESEARCH
01 / MY ECONOMETRICS MASTER’S THESIS
From watching races to modelling them.
Simulating Formula One Race Outcomes
I grew up watching Formula 1 with my dad. For my master’s thesis, I turned that interest into a modelling problem: how could an existing race simulation better reflect the circuit and the conditions?
I extended the baseline with factors such as temperature, elevation and circuit characteristics, then combined the models to simulate complete races and explore different strategies.
2018–2024Historical F1 data
Monte CarloRace simulations
Monza & SuzukaTest circuits
INSIDE THE PROJECTMASTER’S RESEARCH / 2025
Explore how I approached the problem.
THE RACE, BROKEN DOWNSCHEMATIC
START WITH THE COMPONENTS
Make the race model more realistic.
I gathered historical race data in Python and used regression models, ElasticNet for feature selection and probabilistic models. The work covered lap times, fuel effects, tyre degradation, pit stops and race events.
PythonStatistical modellingElasticNet
COMBINE THE MODELS
Explore how a race could unfold.
I brought the components together in a full-race simulation. Monte Carlo simulations allowed me to explore possible outcomes and strategies while accounting for uncertainty, including DNF risks and safety-car scenarios.
Monte Carlo simulationRace strategyUncertainty
CHALLENGE THE ASSUMPTIONS
Compare the model with real races.
I compared my extension with the original approach and tested it against actual Grand Prix results. Monza and Suzuka offered different circuit characteristics. The key lesson was to question whether assumptions still held under different conditions.
My next step: develop this race-data foundation further in Power Unit performance and reliability analysis.
S1 / DATA
02 / MY WORK AT MEIJERS
Dashboards for the people making the decisions.
As a Junior Data Developer, I worked on bringing information from different business systems together. Understanding what the user needed was part of the technical work.
THE DATA PIT WALL
SOURCE SYSTEMS→SQL + SYNAPSE→POWER BI INSIGHTS
Collect. Check. Make it useful.
Junior Data Developer Meijers | October 2023–February 2026
01Bring the data togetherSQL & AZURE SYNAPSE+
I worked with data from several internal systems, including Salesforce. I used SQL to retrieve, combine, check and transform it, with Azure Synapse supporting integration and processing.
Data quality was a recurring part of the job: checking that information was complete, consistent and processed correctly.
SQLAzure SynapseData quality
02Make the result usablePOWER BI & THE USER’S PERSPECTIVE+
I developed and improved dashboards and data models so departments could understand their performance and use the information in their decisions.
I often acted as a bridge between the business and the technical side. I would ask what someone needed to understand, then think about how the dashboard could make that clear.
Power BIData visualisationStakeholder communication
03Keep the work structuredDEVELOPMENT & COLLABORATION+
I initially worked with Qlik Sense and Qlik NPrinting before the transition to Microsoft Azure. Azure DevOps supported development and collaboration within the team.
Across my work and studies, I have also documented and structured code and analyses using Git and Azure DevOps.
Operations Manager AthenaStudies | June 2022–January 2024
At the Landelijke Econometristendag
45 → 60
participating companies in around six weeks, with the committee
45 companies 15 added together
During preparations for the Landelijke Econometristendag, company acquisition was behind schedule. As chairwoman, I mapped the bottlenecks, drew up an action plan with the committee and involved our advisory board.
I learned how much a team can achieve when people have a clear plan and someone takes responsibility for keeping it moving.
AT ATHENASTUDIES
Helping six managers work as one team.
I coordinated six Marketing Managers across university faculties, after starting in a marketing role myself. The work taught me to keep an overview while staying close to what each team needed.
S2 / LEARNING
04 / WHY A SECOND MASTER’S
Why I added AI to econometrics.
I chose a second master’s because I wanted to combine traditional statistics and econometrics with modern AI and machine learning. At Deakin, I’ve gained practical experience with machine learning, computer vision, deep learning and neural networks.
From my bachelor’s to AI. Scroll through my journey ↓
EDUCATION / 01 OF 052020–2023
BSc Econometrics & Data Science
University of Amsterdam
The foundation: econometrics, statistics and data science.SCROLL TO THE NEXT CHAPTER ↓
EDUCATION / 02 OF 05AUGUST 2022–JANUARY 2023
A semester at NTNU
Norwegian University of Science and Technology
Artificial Intelligence, Algorithms and Applied Data Science.SCROLL TO THE NEXT CHAPTER ↓
EDUCATION / 03 OF 052024–2025
Master’s in Econometrics & Data Science
Vrije Universiteit Amsterdam
Master’s thesis: Simulating Formula One Race Outcomes.SCROLL TO THE NEXT CHAPTER ↓
EDUCATION / 04 OF 052025–PRESENT
MSc Artificial Intelligence
Vrije Universiteit Amsterdam
A second master’s, adding AI and machine learning to my econometrics background.SCROLL TO THE NEXT CHAPTER ↓
EDUCATION / 05 OF 05MAY–NOVEMBER 2026
AI exchange in Melbourne
Deakin University
Practical machine learning, computer vision, deep learning and neural networks.NEXT UP / THE PERSON BEHIND THE DATA ↓
S3 / PASSION
05 / BEYOND THE DATA
F1 has always been personal.
I’ve watched the races with my dad since I was little. Visiting Spa at around fourteen made a big impression, and in recent years I’ve kept going back to the Zandvoort Grand Prix.
What fascinates me is everything behind the result: the engineering, the strategy and how a small change in the analysis can make a difference on track. I want to contribute to that work.
THE MEMORIES BEHIND THE MODELS ↓
IN THE GRANDSTANDMore than a race on a screen.
WHERE IT STARTED
14AROUND FOURTEEN MY FIRST GRAND PRIX
Spa-Francorchamps. The interest stayed.
Years of watching races with my dad. Memories at the circuit. And now, a master’s thesis built around the sport.
A RED BULL MEMORYA stop by the team coach.
A MOMENT AT THE CIRCUIT
The view from the grandstand.
A few seconds of the atmosphere that keeps bringing me back.
Tap play for the trackside moment. Sound starts when you play.
Outside my studies, I train regularly, enjoy running and like spending time outdoors. I like setting goals, building discipline and finding out how I can improve.
GYMRUNNINGOUTDOORS
BEYOND THE DATATime outside, a change of perspective.
S3 / NEXT STEP
RED BULL POWERTRAINS / DATA STRATEGY & INSIGHTS
Let’s talk data and racing.
I’m planning for a July 2027 start and would be happy to relocate to Milton Keynes. Final timing will depend on the completion of my AI master’s thesis.
Junior Data Developer
