Data Scientist

WorldWinner

13d • Remote

About The Position

We are seeking a Data Scientist to join our analytics organization and serve as the technical modeling engine behind our player experience, monetization, and growth strategies. This is a hands-on individual contributor role embedded in a tight-knit analytics team, working directly with the Director of Business Performance on problem framing, prioritization, and execution.

What You'll Work On
You will own the modeling workstreams that sit at the core of how WorldWinner understands and serves its players. Initial scope includes:
Fair matching algorithm — modeling and improving skill-based player matching to create competitive, satisfying game experiences
Skill ranking system — developing and refining the rating framework that underpins player progression and matchmaking
Predictive LTV — building models that forecast player lifetime value to inform acquisition, targeting, and monetization decisions

As you grow into the role, your scope will expand to include:
Churn prediction — identifying at-risk players and surfacing actionable signals for retention intervention
Mixed marketing model (MMM) — measuring the incremental impact of marketing spend across channels
Additional modeling projects driven by product and business priorities

How You'll Work
This role is not a silo. You will work closely with the Director of Business Performance throughout the lifecycle of every project — from framing the business question and scoping the approach, to communicating progress, surfacing blockers early, and presenting findings to stakeholders. We move fast, operate with high trust, and expect regular two-way communication on what you're building and why.

You will be expected to represent the analytics function in cross-functional meetings — with product, marketing, and engineering — without needing translation. That means explaining model logic to non-technical audiences, translating business questions into analytical approaches, and advocating for data-driven decisions in rooms where not everyone speaks statistics.

What Makes Someone Successful Here
The data scientists who struggle in this role are the ones who disappear and resurface with a model deck. The ones who thrive are curious, communicative, and collaborative — they share work early, ask questions before making assumptions, and care as much about the business outcome as the model performance metric. If you've been told your superpower is making complex ideas accessible, this is the right place.

Requirements

• 4+ years of experience in applied data science, with a track record of shipping models that drove measurable business outcomes
• Strong proficiency in Python for modeling, statistical analysis, and data manipulation
• Advanced SQL skills and comfort working directly with large-scale data warehouses (Redshift experience a plus)
• Experience building and validating predictive models — LTV, churn, propensity, recommendation, or ranking systems
• Demonstrated ability to communicate technical work clearly to non-technical stakeholders — in writing, in meetings, and in executive presentations
• Comfort working in a collaborative, fast-moving environment with regular check-ins and shared prioritization
• Gaming industry experience — mobile gaming, real-money gaming, skill gaming, social casino, or live-service products

Nice To Haves

• Experience with matching algorithms, ELO/Glicko-style rating systems, or similar player ranking frameworks
• Background in marketing mix modeling or multi-touch attribution
• Familiarity with Hex for analysis and model deployment workflows
• Experience with AWS SageMaker or similar cloud ML infrastructure
• Exposure to experimentation design and A/B testing methodology

Responsibilities

• modeling and improving skill-based player matching to create competitive, satisfying game experiences
• developing and refining the rating framework that underpins player progression and matchmaking
• building models that forecast player lifetime value to inform acquisition, targeting, and monetization decisions
• identifying at-risk players and surfacing actionable signals for retention intervention
• measuring the incremental impact of marketing spend across channels
• Additional modeling projects driven by product and business priorities

Benefits

• Exciting, creative, and fun industry where you can make a measurable impact.
• Collaborative and inclusive work environment.
• Comprehensive subsidized medical, dental, and vision coverage with paid parental leave options.
• 5% company 401(k) match with immediate vesting.
• Generous time off and flexible hours to support work-life balance.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed