Head of Data

We’re a rapidly scaling digital product company transforming the rewarded advertising category. We’ve grown quickly over the last few years and are expanding the team with people who want to build, ship, and improve high-quality digital experiences.

We’re looking for a Head of Data to create and lead the company’s Data d function from the ground up — owning the strategy, delivering production-grade models, and building the team that will scale those capabilities over time.

This is a hands-on leadership role sitting at the intersection of product, engineering, and analytics, with real ownership over initiatives that impact engagement, monetization, and risk controls.

You’ll start by managing an external data agency, while laying the foundation for an internal team. You’ll also lead the roadmap for applied AI/LLM-powered features and internal AI tools.

What you’ll do

Strategy and leadership

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.

Machine learning & personalization

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

Fraud detection & risk systems

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.

Data foundations & Operations

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).

What you bring

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.
Experience collaborating cross-functionally and managing external partners/vendors.

What you’ll get

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).