Role Overview

We're looking for a Data Scientist to own key modeling and analytics initiatives across Heylist. You'll build and deploy machine learning models for creator matching, campaign forecasting, audience segmentation, and content performance. You'll partner with Engineering to bring models into production, with Product to prioritize the highest-impact bets, and with Marketing and Customer Success to turn insights into stories our customers can act on.

Key Responsibilities

1. Modeling & Machine Learning

• Design, build, and deploy ML models for creator discovery, matching, and performance
• Own the full model lifecycle, from problem framing to production monitoring
• Advance our capabilities in embeddings, ranking, and recommendation systems
• Explore generative AI use cases across product and internal workflows

2. Analytics & Insights

• Analyze first-party and social platform data (Meta, TikTok, and others)
• Surface actionable insights for Product, GTM, and executive stakeholders
• Design experiments and drive rigorous causal analysis
• Build dashboards and internal tools that make data accessible

3. Productionization & MLOps

• Partner with Engineering to productionize models on our cloud stack (Azure)
• Establish standards for model evaluation, monitoring, and retraining
• Ensure responsible use of AI and appropriate handling of sensitive data

4. Team Building

• Contribute to the data science roadmap and strategy
• Mentor peers and set best practices as the team grows
• Collaborate across Data Engineering and Product on shared foundations

What You'll Own

• Data science strategy and roadmap
• Model quality, reliability, and business impact
• Best practices for experimentation and evaluation

Requirements

• 7+ years in Data Science, Machine Learning, or Applied ML (SaaS, MarTech, AdTech, or consumer platforms)
• Degree in Computer Science, Statistics, Mathematics, or a related quantitative field, or equivalent experience
• Strong programming skills in Python and modern ML/data stacks (pandas, scikit-learn, PyTorch or TensorFlow)
• Solid grounding in statistics, experimentation, and causal inference
• Experience deploying models to production on cloud infrastructure (Azure)
• Experience with SQL and relational databases (Postgres)
• Excellent communication skills for translating modeling work into business narratives

Nice to Have

• Generative AI, LLMs, embeddings, or recommendation systems
• Social platform APIs (Meta, TikTok)
• MLOps tooling and infrastructure-as-code (Terraform)
• Prior work in influencer marketing, creator economy, or social analytics

Success Metrics

• Business impact of models shipped (revenue, adoption, engagement)
• Model quality, latency, and reliability in production
• Speed from problem to shipped model
• Adoption of experiments and insights across the company