Games Factory Talents | Connecting Passion & Talent

Head of Data

Games Factory Talents | Connecting Passion & Talent

full-time

Posted on:

Location Type: Remote

Location: United States

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Job Level

Lead

Tech Stack

AirflowBigQueryPythonPyTorchTensorflow

About the role

  • Define the data science vision and roadmap (what to build, why, and in what order).
  • Hire, mentor, and set standards for a high-performing Data team.
  • Own vendor delivery and coordination (external agency) until the internal team is established.
  • Partner closely with product, engineering, marketing, operations, fraud, and leadership; turn business goals into measurable outcomes.
  • Communicate complex model tradeoffs clearly to non-technical stakeholders.
  • Lead the applied AI/LLM direction for product experiences and internal tooling.
  • Build and maintain models for personalization/recommendations, engagement prediction, churn, and LTV/forecasting.
  • Own the full model lifecycle: training, evaluation, deployment, monitoring, and retraining.
  • Work with engineering to integrate models into backend services and client experiences with performance and scalability in mind.
  • Design and ship LLM-enabled capabilities (e.g., semantic search, assistants, automation, content generation, decision support).
  • Evaluate and adapt foundation models (fine-tuning/distillation/prompting) for product and internal workflows.
  • Develop anomaly detection and behavioral models to identify suspicious activity.
  • Create scoring and classification systems for patterns like manipulation, multi-accounting, and other abuse signals.
  • Partner with product/ops to design thresholds, rules, validation systems, and automated risk workflows.
  • Track emerging fraud trends and proactively address new attack vectors.
  • Define requirements for scalable pipelines supporting training, inference, experimentation, and monitoring.
  • Implement practical MLOps standards (CI/CD for models, versioning, monitoring, automated retraining).
  • Raise the bar for data quality, documentation, and metadata practices.
  • Build infrastructure patterns for LLM workloads (vector search, embeddings pipelines, evaluation, safety checks).

Requirements

  • 6+ years in Data Science / ML Engineering, including 2+ years in a senior ownership or leadership capacity.
  • Strong Python and hands-on experience with ML frameworks (e.g., PyTorch/TensorFlow/XGBoost or equivalents).
  • A track record of deploying and operating ML models in production.
  • Expertise in one or more of: recommendations/personalization, time-series forecasting, or fraud/risk modeling.
  • Familiarity with modern data stacks and pipeline tools (e.g., Airflow/dbt/BigQuery/Snowflake or similar) and MLOps best practices.
  • Strong experimentation mindset and ability to connect modeling work to measurable business impact.
Benefits
  • The chance to define and own a data science + AI function with major product impact.
  • Direct collaboration with senior leadership and product/engineering stakeholders.
  • Fully remote, full-time role with a structured time-off policy (vacation, personal days, sick leave, parental leave, local holidays).
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
data sciencemachine learningPythonML frameworksmodel lifecycleanomaly detectionrecommendationspersonalizationtime-series forecastingfraud modeling
Soft Skills
leadershipmentoringcommunicationcollaborationexperimentation mindset