Senior Data Scientist - User Insights (Hybrid, ML & Recs)

Prime Talent Recruiting; Irvine (CA); Hybrid; USD 150,000 - 180,000; Full time

Benefits offered by this job

Remote-friendly

Job summary

Prime Talent Recruiting seeks a Data Scientist - User Insights to design, train, and deploy scoring and recommendation models that connect users with personalized experiences across SaaS and mobile apps.

The role requires 5+ years deploying large-scale ML systems, with expertise in recommender systems, predictive modeling, and ad-tech, plus hands-on Generative AI and ML Ops on AWS. Collaboration with engineering and product teams is essential to scale impact.

Qualifications

• 5+ years designing, building, and deploying large-scale ML systems, with preference for recommender systems, predictive modeling, or ad-tech.
• Experience applying ML to real-world problems beyond theory and mathematics.
• Proven track record of bringing ML solutions to market.
• You need to deliver data in ways engineers can easily use and integrate into their systems.

Responsibilities

• Design, train, and deploy scoring and recommendation models that connect users with relevant offers and personalized experiences in an application.
• Practical experience with Generative AI and large language model workflows.
• Collaborate with engineering and product teams to develop scalable ML systems aligned with product goals.
• Move ML models from prototype to production, delivering real business results.
• Work with Python and tools like Pandas, PyTorch, TensorFlow, plus SQL and big data platforms.
• Experience ML Ops on AWS with Docker, Kubernetes, Airflow, Kubeflow.

Skills

Python

SQL

Pandas

PyTorch

TensorFlow

Generative AI

Recommender systems

ML model deployment

Tools

Docker

Kubernetes

Airflow

Snowflake

Spark

Job description

Prime Talent Recruiting seeks a Data Scientist - User Insights to design, train, and deploy scoring and recommendation models that connect users with personalized experiences across SaaS and mobile apps.

The role requires 5+ years deploying large-scale ML systems, with expertise in recommender systems, predictive modeling, and ad-tech, plus hands-on Generative AI and ML Ops on AWS. Collaboration with engineering and product teams is essential to scale impact.