Data Scientist, Next Gen Recommendation Systems (AI)

Location: New York City, United States

Salary: $100,000–$125,000 per year, plus a 5% variable annual bonus and potential RSU grant.

Company

hirify.global provides a commerce partnership marketing platform connecting brands with affiliates, influencers, publishers, creators, and customer advocates.

What you will do

• Design, build, and evaluate recommendation and ranking models across advertisers, publishers, creators, products, and consumers.
• Develop graph-based modeling approaches using semantic embeddings, representation learning, graph neural networks, and graph transformers.
• Build batch and real-time recommendation pipelines using retrieval infrastructure, vector search, feature stores, and low-latency serving patterns.
• Own the ML lifecycle from data and feature design through productionization, monitoring, iteration, and reliability improvements.
• Design offline evaluations and online experiments, including A/B tests, holdouts, interleaving, and counterfactual evaluation.
• Collaborate with Product, Engineering, MLOps, and business stakeholders to connect model outcomes with measurable platform value.

Requirements

• 3+ years of experience in data science or applied ML, including shipping production models with measurable impact.
• Strong Python and SQL skills, with experience handling large-scale data and distributed compute such as Spark or Databricks.
• Hands-on experience with recommendation or ranking systems, including candidate generation, learning-to-rank, retrieval, reranking, or implicit feedback modeling.
• Experience with embeddings and representation learning for users, items, content, or other entities.
• Ability to build, ship, and maintain production ML pipelines, supported by strong experimentation and statistical analysis skills.
• Regular use of AI coding agents, strong problem-solving skills, curiosity, and the ability to work autonomously in ambiguous environments.

Nice to have

• Graph-based ML, knowledge graph embeddings, graph-aware retrieval, or graph neural networks.
• Modern deep learning recommender architectures, including two-tower, sequence-based, transformer-based, or multi-task ranking models.
• Vector databases, approximate nearest neighbor methods, real-time ML serving, feature stores, or low-latency inference.
• Contextual bandits, reinforcement learning, off-policy evaluation, PyTorch, TensorFlow, PyTorch Geometric, or DGL.
• GCP, mature MLOps practices, or experience in adtech, martech, e-commerce, or marketplace recommendations.

Culture & Benefits

• Medical, dental, and vision insurance, flexible spending accounts, and a 401(k).
• Responsible PTO policy and flexible working environment focused on work-life balance.
• Mental health and wellness support, including covered therapy or coaching sessions and gym reimbursement.
• RSUs with a three-year vesting schedule, subject to Board approval.
• Coursera subscription, PXA courses, paid parental leave, technology stipend, and monthly internet allowance.
• Office-based catered lunch every Thursday, snacks, and coffee.