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Data Scientist - User Insights

Job in Irvine, Orange County, California, 92713, USA
Listing for: Prime Talent Recruiting
Full Time position
Listed on 2025-12-12
Job specializations:
  • IT/Tech
    Machine Learning/ ML Engineer, AI Engineer, Data Scientist, Data Engineer
Salary/Wage Range or Industry Benchmark: 150000 - 180000 USD Yearly USD 150000.00 180000.00 YEAR
Job Description & How to Apply Below

About the job Data Scientist - User Insights

Data Scientist - User Insights

Salary $150k -$180k

Position :
Orange County - Hybrid

Were looking for a Data Scientist to develop and deploy machine learning models that drive personalization and recommendation insights across SaaS and mobile app platforms.
Candidate must be experienced in applying machine learning to real-world problems beyond just theoretical and mathematical concepts

Over 5 years of experience designing, building, and deploying large-scale ML systems, with a preference for expertise in recommender systems, predictive modeling, or ad-tech.

Proven track record of successfully bringing machine learning solutions to market.

You need to deliver data in a way that engineers can easily use and integrate into their systems

What youll do:

Design, train, and deploy scoring and recommendation models that connect users with relevant offers and personalized experiences in an application.

Practical experience working with Generative AI and large language model workflows.

Collaborate with engineering, product teams to develop scalable machine learning systems that align with product goals.

Successfully moved several machine learning models from prototype to production, delivering real business results.

Skilled in Python and its tools like Pandas, PyTorch and Tensor Flow.

Strong SQL knowledge and experience working with big data platforms like Spark and Snowflake.

Experience working hands‑on with Generative AI and large language model workflows, including prompt engineering and fine tuning.

Experience with ML Ops on cloud platforms such as AWS, including container tools like Docker and Kubernetes, and workflow tools like Airflow and Kubeflow.

Experienced in integrating model services with scalable, version‑controlled APIs for reliable deployment and maintenance.

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