Data Science Product Manager Jobs in Missouri (NOW HIRING)

• NLP & Utterance Analysis: Leverage Natural Language Processing (NLP) and machine learning to categorize and cluster raw user utterances. Perform sentiment analysis on unstructured text logs to extract actionable product insights.
• AI Response Evaluation & Experimentation: Design methodologies to evaluate the helpfulness, accuracy, and relevance of the AI’s responses. Design and analyze A/B tests to measure the impact of prompt adjustments, model updates, and new feature rollouts.
• Data Integration & Sanitization: Dive directly into Google Cloud Platform (GCP) to cleanly join and structure mobile, customer support, and vehicle data into robust "Analytical Sandboxes," ensuring strict adherence to data privacy and PII handling standards.
• Problem Framing & Metric Definition: Act as a strategic partner to Product Managers. Challenge assumptions and define core conversational metrics (e.g., task success rates, user engagement, support deflection).
• Advanced Visualization & Self-Service: Design, build, and maintain highly intuitive, narrative-driven dashboards using Looker and PowerBI to empower the product team to answer their own day-to-day questions.
• Bridge the Mobile-to-IVI Gap: Act as the analytical bridge as our digital assistant expands from the Ford app into the vehicle, standardizing mobile data against our emerging in-vehicle data contracts.

Requirements

• Education: A Master's Degree in a quantitative, technical, or related field (e.g., Data Science, Computer Science, Statistics).
• Experience: 7+ years of experience in Data Science, Product Analytics, or Applied Machine Learning.
• "Full-Stack" Capability: Demonstrated ability to act as a bridge between Data Science, Engineering, and Product—taking raw telemetry, applying statistical/ML models, and transforming it into business insights without relying on a central data team for every step.
• Applied ML & LLM Analytics: Proficiency in Python or R with hands‑on experience in text analytics, clustering, and categorization. Familiarity with LLM evaluation techniques (e.g., prompt effectiveness, hallucination tracking, human-in-the-loop feedback).
• Expert‑Level SQL & GCP: Highly proficient in writing complex, optimized SQL (Window Functions, CTEs, handling JSON/Nested Data) within Google Cloud Platform (BigQuery) to structure datasets independently.
• Advanced Visualization: Deep expertise in building scalable business intelligence solutions, semantic layers, and executive‑facing dashboards in Looker and PowerBI.
• Experimentation: Strong grasp of statistics and experience designing and measuring A/B tests in a product environment.
• Analytics as Code: Experience with version control (e.g., Git, GitHub) and working in environments where analytics changes go through a formal peer‑review process.
• Strategic Problem Solving: Comfortable navigating complex, multi‑source data environments. You view data integration as a puzzle to be solved and a strategic enabler for the business.

Core Competencies

Demonstrates expertise in Natural Language Processing, Applied Machine Learning, and Data Analytics, with a strong focus on building advanced visualizations and conducting A/B testing to drive product insights. Proficient in SQL and Google Cloud Platform, capable of integrating and structuring complex datasets while ensuring data privacy standards.

Highest‑signal resume keywords

• Natural Language Processing
• Applied Machine Learning
• Expert‑Level SQL
• Google Cloud Platform
• Advanced Visualization

ATS Optimization Keywords

Hard Skills

• Natural Language Processing
• Machine Learning
• SQL
• Python
• R
• Data Analytics
• A/B Testing
• Text Analytics
• Clustering
• Data Integration

Soft Skills

• Strategic Problem Solving
• Collaboration

Industry Keywords

• Data Science
• Product Analytics
• Sentiment Analysis
• Data Privacy
• PII Handling

Tools & Technologies

• Google Cloud Platform
• Looker
• PowerBI
• Git
• GitHub