Data Scientist

Data Scientist

This is a 12-month, full-time (40 hours/week), remote contract role located within 100 miles of Culver City, CA.

The Digital Marketing team is seeking a Data Scientist to help analyze large-scale consumer feedback, uncover insights that inform marketing and product decisions, and monitor external brand relevance. This role focuses on applying advanced statistical methods, machine learning, and natural language processing to identify meaningful trends and translate complex data into actionable insights that support strategic decision-making.

• 3-5 years of experience in data science, machine learning, or advanced analytics
  
• Passion for data-driven problem solving and marketing analytics with a strong foundation in statistics and machine learning techniques
  
• Proficient in SQL and Python or R, with strong data modeling and large dataset analysis skills
  
• Familiar with digital marketing metrics including social, SEO, and paid media performance
  
• Skilled in collaborating with cross-functional teams including marketing, product, creative, and analytics partners
  
• Able to work independently and manage multiple projects simultaneously
  
• Flexible and adaptable to changing priorities, timelines, and stakeholders
  
• Strong written and verbal communication with exceptional attention to detail
  
• Experience presenting analytical findings and translating complex models into clear insights for diverse audiences
  

In this role, you will develop and maintain data-driven models that optimize marketing performance and uncover insights from large and complex datasets. You'll partner with marketing and product teams to analyze public consumer feedback and translate insights into strategic recommendations that improve campaign effectiveness and brand relevance.

This includes building optimization models, experimentation frameworks, forecasting algorithms, and machine learning models to identify trends and improve marketing strategy. You will also perform causal analysis to evaluate the impact of marketing initiatives across both organic and paid channels.

You will design and refine models that measure upper-funnel marketing impact by integrating multiple metrics and diverse structured and unstructured datasets. Additionally, you will collaborate with cross-functional teams to propose analytical solutions to business challenges while translating model outputs into actionable insights and reporting for stakeholders.

• Bachelor's degree in Data Science, Computer Science, Statistics, or a related field with 3 years of experience, or a Master's degree in Data Science, Computer Science, or a related field with 2 years of experience
  
• Background in machine learning, statistical modeling, and large-scale data analysis
  
• Experience working with supervised and unsupervised machine learning algorithms, including regression, classification, clustering, decision trees, and neural networks
  
• Preferred experience with causal inference techniques, marketing mix modeling, or multi-touch attribution models
  
• Familiarity with model evaluation frameworks, sampling strategies, and human-in-the-loop processes
  
• Experience using version control systems such as Git, GitHub, or GitLab
  
• Ability to read and analyze large amounts of content in Simplified Chinese is a plus
  

• This opportunity is remote but requires candidates to be located within 100 miles of Culver City, CA
  
• The pay for this W-2 position is $64.24 per hour
  
• This position may be eligible for PTO, health and dental insurance, and/or 401(k) benefits upon meeting certain length of service and hours requirements.
  

Mathys+Potestio values applicants of all backgrounds and experiences. We do not discriminate based on race, color, national or ethnic origin, ancestry, age, religion or religious creed, disability or handicap, sex or gender, gender identity and/or expression, sexual orientation, military or veteran status, genetic information, or any other characteristic protected under applicable federal, state, or local law.