Somebody has to explain why the line moved. On a good day, it's obvious — an injury report, a weather call, a lopsided ticket count. Most days it isn't, and that's where a data analyst earns their pay. We're looking for a
Remote Sports Betting Data Analyst who can sit with a messy market and tell us what's actually happening underneath it.
The Basics
Fully remote.
$128,500 per year. Open to candidates in the United States, Canada, the United Kingdom, the European Union, Australia, India, and several other regions — wherever you are, if the fit's right, distance isn't the issue.
What You'd Be Doing
The job splits roughly into two halves: understanding what already happened, and predicting what's about to happen. In the first half, you'll dig through betting data using exploratory analysis and statistical modeling, and keep an eye on live feeds for shifts in behavior across platforms. When something's worth flagging, you'll bring it to traders, risk teams, and content staff and help turn it into strategy.
The second half is more forward-looking:
- Build and refine machine learning models to predict event outcomes and market behavior.
- Adjust those models as odds shift, event types change, and bettor patterns evolve — because they always do.
- Run structured tests on competing forecasting methods before trusting any of them live.
- Design dashboards that make dense data readable at a glance, not a project in itself.
- Put together weekly reports on anomalies, market swings, and player performance for people who need the summary, not the raw table.
- Bring visuals into product planning so decisions happen with the data in front of everyone, not after the fact.
- Chase down unconventional data — social sentiment, fan behavior, even weather — when the usual sources run dry.
Education and Experience
You'll need a
Bachelor's degree at minimum, plus at least
3 years of relevant experience in data analytics, sports analytics, or a related field. A betting or gaming background helps, but it's not a hard requirement if your analytical fundamentals are solid.
What You Should Bring
You can write efficient SQL and pull exactly what you need out of a large, complicated database without much hand-holding. Python or R should feel like second nature for cleaning data, building models, and putting together visuals. You've used something like Power BI or Tableau enough to know what makes a dashboard actually useful versus just busy. Cloud environments such as AWS Redshift or Google BigQuery don't intimidate you. And you understand, at a real level, how odds get set and what drives the psychology behind a bet.
Nice to Have, Not Required
- You've connected to a sports data provider's API before and kept a live feed stable under pressure.
- Direct experience with live or in-play betting analytics.
- A metric or indicator you proposed actually influenced how odds got set somewhere.
- Genuine interest in micro-markets and forecasting methods that haven't gone mainstream yet.
- Enough sports knowledge to anticipate seasonal quirks across major leagues without looking them up.
How Remote Actually Works Here
Work from wherever you focus best. That said, the team isn't scattered and silent — expect regular working sessions and a Slack channel that gets a little too opinionated about underdog bets some weeks. Postings like this one, when found through
Naukri Mitra, tend to attract people who want independence without losing the collaboration, and that balance is real here, not just a line in a job ad.
Compensation and Perks
- Flexible hours structured around a few shared collaboration windows.
- Performance-based bonuses layered on top of base pay.
- A yearly stipend toward courses, certifications, or conferences.
- Quarterly wellness allowance, because remote work needs boundaries too.
- Home office and tech allowance so equipment isn't your limiting factor.
- Complimentary premium sports streaming access — useful for the job, not just a nice-to-have.
Add direct access to research tools and analysts who've been doing this longer than most, and the growth here is real rather than promised.
Who You'd Work With
Expect colleagues who push back. Someone will insist social sentiment predicts line movement; someone else will want proof before they buy it. That friction is intentional — people here would rather be corrected by data than agree just to keep things smooth.
How to Apply
Apply with your resume and, if you've got them, a model or dashboard you're proud of. We review every application ourselves, and if it looks like a fit, you'll hear back directly.
