Head of Data
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Job Description
Head of Data creating and leading a Data function for a rapidly scaling digital product company. Overseeing data strategy, AI features, and machine learning initiatives.
Responsibilities:
- 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).
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