LoL Trading Analyst – Esports (Odds & Market Performance) (m/f/x)
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 3h ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 38d ago
The date the source published, not the day we noticed it (2026-09-04). Last seen at its source just now.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
Poland, Peru, Czechia
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay $100.0k–143.5k/yr
Middle 50% of 13 listings that do state pay — Finance · all levels · Eastern Europe · USD/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
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Recruitee employer's own board first seen 3h ago · last seen just now
The listing
Can you identify pricing inefficiencies in MOBA markets by combining game knowledge with data analysis?
Do you thrive on turning messy match data into actionable insights that improve odds models?
Are you passionate about League of Legends and the wider competitive MOBA ecosystem?
If the answers to the above questions are yes, then this role could be ideal for you!
About the role
Support the modelling and trading teams by analysing MOBA match data and market behaviour to improve odds accuracy, market efficiency, and operational decision-making.
You will work closely with live and pre-match traders across MOBA titles, with a particular focus on League of Legends, to identify anomalies, optimise odds, monitor performance, and provide insights that directly impact P&L across GRID’s MOBA betting products.
What you will do
Analyse MOBA match behaviour to improve pricing accuracy and market efficiency
Conduct variance analyses and trading performance reviews
Validate and monitor odds models for MOBA titles under real trading conditions
Detect model drift, pricing inefficiencies, and performance anomalies affecting P&L
Prepare actionable insights to support traders and trading decisions
Monitor competitor offerings and identify opportunities to improve trading strategies
Analyse match outcomes, meta shifts, patch changes, draft trends, and gameplay developments
Evaluate League of Legends-specific factors such as champion drafts, item builds, map objectives, team compositions, gold advantages, and macro strategy
Proactively identify how gameplay, patch, or rule changes could affect our models and address potential issues before they arise
Suggest enhancements to pricing models through new algorithms or investigations with external feed providers where applicable
Translate analytical findings into clear and compelling narratives for technical and non-technical audiences
Ensure all processes and findings are documented to a high standard and are accessible to both technical and non-technical stakeholders
Support ad hoc trading and market-analysis requests from the MOBA trading team
Your skills will include
3+ years of experience in sports betting, trading, quantitative analysis, or a related field
Deep understanding of League of Legends, including champion mechanics, item builds, drafting, map objectives, team compositions, macro strategy, tournament formats, and player behaviour
Strong knowledge of the professional League of Legends ecosystem, including regional leagues, tournament structures, current meta trends, and patch-driven gameplay changes
Solid understanding of trading fundamentals, including pricing, margin, overround, and risk models
Strong statistical, mathematical, and analytical skills, with practical applications in pricing and risk
Experience with statistical modelling and data analysis
Strong attention to detail and the ability to retain complex information
A positive, can-do attitude and the ability to meet deadlines
Willingness to learn and adapt to new environments
Nice to have
Knowledge of other competitive MOBA titles and esports ecosystems
Bachelor’s degree in Mathematics, Statistics, Economics, Data Science, or a related field
Familiarity with machine-learning concepts and applications
SQL skills or experience with Python, R, or similar analytical tools