Data Scientist, Growth Analytics

Tubi•Los Angeles, CA

14h•Hybrid

About The Position

We are looking for a problem solver who thrives at the intersection of data science, growth strategy, and AI-powered automation. You’ll focus on diving deep into data, creating scalable data solutions, building agentic AI workflows, supporting growth marketing measurement, and enabling growth experimentation to power smarter decision-making for growth marketing. This is a highly technical and data-intensive role ideal for someone who is passionate about applying advanced analytics and AI to drive business impact in a fast-paced environment. This is a hybrid role based out of our San Francisco or Los Angeles office. You must be willing to travel to the office two days/week.

Requirements

4+ years of experience in Analytics, Data Science, Decision Science, or Growth Analytics, with demonstrated impact in a high-growth B2C technology company. (streaming, gaming, fintech, or consumer app businesses preferred).
Deep expertise in SQL, including complex query optimization, dimensional modeling, and large-scale analytical data warehouses such as Databricks, Snowflake, BigQuery, or Redshift.
Strong programming experience in Python for statistical modeling, automation, experimentation, AI applications, and production-quality analytics workflows.
Experience building AI-powered analytical tools, agentic workflows, or LLM-based applications that automate repetitive analysis, accelerate decision-making, or improve business operations.
Proven experience building predictive models and applying machine learning techniques to solve business problems in marketing, growth, or customer lifecycle optimization.
Deep understanding of experimentation methodologies, including A/B testing, causal inference, Bayesian statistics, incrementality testing, and observational analysis.
Experience developing executive-facing dashboards and scalable self-service analytics solutions using Tableau, Preset, or similar BI platforms.
Demonstrated ability to influence cross-functional strategy and communicate complex analytical concepts to executive and non-technical audiences.
Strong curiosity, autonomy, and passion for building scalable analytics capabilities and establishing best practices that raise the bar across the organization.

Nice To Haves

Familiarity with modern AI frameworks and tooling (e.g., LangChain, LangGraph, OpenAI APIs, MCP, vector databases, or similar agent orchestration frameworks) is highly desirable.
Strong understanding of digital marketing measurement, attribution methodologies, media performance, and customer acquisition economics is a plus.

Responsibilities

Lead the design and evolution of measurement frameworks that quantify marketing effectiveness across the full customer lifecycle, including acquisition, activation, retention, and brand impact.
Create business intelligence suites that provide a holistic view of marketing performance across growth marketing, brand marketing, Search/Answer Engine Optimization (SEO/AEO), Customer Relationship Management (CRM), social, content, partnerships, and business development, spanning both performance and brand health metrics.
Build scalable analytics products that provide executive visibility into marketing performance across paid, owned, and earned channels.
Design and implement agentic AI solutions that automate recurring analyses, generate actionable insights, orchestrate multi-step analytical workflows, and accelerate decision-making for growth marketing, brand marketing, SEO/AEO, CRM, social, and broader marketing operations teams.
Leverage large language models (LLMs), AI agents, and modern AI frameworks to improve productivity, automate reporting, enhance experimentation workflows, and surface intelligent recommendations.
Evaluate and implement advanced statistical techniques - including Bayesian modeling, causal inference, uplift modeling, and media mix modeling - to improve marketing measurement and optimization.
Drive the organization's experimentation strategy by designing rigorous A/B tests, incrementality studies, geo experiments, and causal inference methodologies to measure both short-term performance and long-term business impact.
Partner closely with Data Engineering to design robust data models, improve data quality, and define scalable pipelines that support analytics at scale.
Build and maintain dashboards and self-service analytics solutions in Preset, Databricks, or Tableau that empower growth marketing, brand marketing, SEO/AEO, CRM, social, and cross-functional partners with timely, actionable insights.