Data Scientist

About the role

Hi, We're Centerfield!

Supercharged customer acquisition. Centerfield delivers outcome-based digital marketing solutions and personalized omnichannel experiences for the world’s leading brands. Powered by our proprietary Dugout platform, Centerfield acquires customers at scale for leading residential service, insurance, e-commerce, and B2B brands. Centerfield’s digital experiences and digital brands, such as Business.com and BroadbandNow.com reach more than 150 million in-market shoppers annually. Centerfield is headquartered in Silicon Beach and is proud to be recognized by Built in LA as a Best Place to Work in Los Angeles.

Role Overview

We’re looking for a Data Scientist with 2+ years of experience to turn complex data into models, insights, and recommendations that improve business performance. You’ll tackle problems involving customer behavior, marketing effectiveness, sales conversion, and forecasting, partnering with business stakeholders, data engineers, and machine learning engineers.

This role combines a strong foundation in statistics and machine learning with the ability to apply AI and large language models (LLMs) to practical business problems.

Core Responsibilities

• Partner with marketing, sales, and operations teams to define business problems, establish success metrics, and identify opportunities to improve conversion, revenue, and efficiency.
• Explore and combine data from multiple sources using SQL and Python. Investigate data quality issues, develop features, and document assumptions.
• Build and evaluate predictive models for use cases such as customer propensity, lifetime value, segmentation, and forecasting.
• Apply statistical analysis and experimentation to understand performance drivers, measure impact, and communicate uncertainty and limitations.
• Develop reusable analytical workflows and automate recurring analyses to support faster, more consistent decision-making.
• Collaborate with engineering teams to integrate models into business workflows and monitor their performance over time.
• Apply AI and LLMs to use cases such as extracting insights from unstructured data and supporting analytical workflows. Evaluate output quality, reliability, cost, and appropriate handling of data.
• Present findings to technical and nontechnical audiences, explain trade-offs, and recommend clear next steps.

Job Responsibilities

• 2+ years of experience in data science or a closely related role applying statistical analysis and machine learning to business problems.
• Strong SQL skills and proficiency in Python for data preparation, analysis, modeling, and evaluation.
• A solid foundation in statistics and machine learning, including regression, classification, clustering, and hypothesis testing.
• Experience with feature engineering, model validation, and selecting appropriate evaluation metrics, including an understanding of overfitting and data leakage.
• Ability to work with AI and LLMs, demonstrated through professional, academic, or personal projects. Familiarity with prompting, working with model outputs, and evaluating their accuracy and reliability.
• Ability to translate ambiguous business questions into a structured analytical approach and actionable recommendations.
• Attention to data quality, reproducibility, and documentation, with familiarity using version control.
• Strong communication skills and the ability to collaborate across technical and business teams.

Bonus Points

• Experience with GCP, BigQuery, or comparable cloud data platforms.
• Experience in digital marketing, customer acquisition, sales, or customer analytics.
• Experience with forecasting, propensity modeling, customer lifetime value, or experiment design.
• Familiarity with LLM APIs, embeddings, retrieval-augmented generation (RAG), or evaluating AI-powered applications.
• Experience partnering with engineering teams to deploy and monitor models.