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Retail Analytics Jobs in California (NOW HIRING)

Manager, Analytics (Retail, Brand, Innovation & Affiliate) Role: Manager, Analytics Department: Strategy & Analytics Reports To: Senior Director, Strategy & Analytics Location: US (Hybrid/Remote as ...

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Retail Analytics information

See California salary details

$73

$80

$87

How much do retail analytics jobs pay per hour?

As of Jul 18, 2026, the average hourly pay for retail analytics in California is $80.79, according to ZipRecruiter salary data. Most workers in this role earn between $77.31 and $84.28 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in Retail Analytics, and why are they important?

To thrive in Retail Analytics, you need strong analytical skills, statistical knowledge, and a background in business, mathematics, or a related field. Proficiency with data analysis tools such as SQL, Microsoft Excel, Tableau, and statistical software like R or Python is typically required. Effective communication, problem-solving, and attention to detail are crucial soft skills for translating data insights into actionable business strategies. These skills and qualities are essential for driving data-informed decisions that optimize sales, inventory, and customer experience in the retail industry.

Is 40 too old to become a data analyst?

Age is not a barrier to becoming a data analyst, as the role values skills in data analysis, programming, and statistical tools that can be learned at any age. Many professionals transition into data analysis later in their careers by acquiring relevant certifications and gaining experience with tools like Excel, SQL, and Python.

What does a retail data analyst do?

A retail data analyst collects, analyzes, and interprets sales, customer, and inventory data to identify trends and support business decisions. They use tools like Excel, SQL, and data visualization software to generate reports and provide insights that help improve sales performance and operational efficiency.

What does a retail analyst do?

A retail analyst examines sales data, customer trends, and inventory levels to help retail businesses improve performance and profitability. They use tools like Excel and data analysis software to identify patterns and support decision-making, often working closely with marketing and operations teams.

How does a Retail Analytics professional typically collaborate with other departments to drive business decisions?

Retail Analytics professionals frequently work alongside teams such as marketing, merchandising, operations, and supply chain. They gather and analyze data to uncover trends in customer behavior, sales performance, and inventory levels, then present actionable insights to support strategic planning. Effective communication and cross-functional collaboration are essential, as analytics professionals must translate complex data findings into clear recommendations that other departments can implement. This collaborative approach ensures that data-driven decisions align with overall business goals.

What is retail analytics?

Retail analytics is the process used by retail professionals to analyze data related to customer behavior, sales, inventory, and store operations. It involves using tools like data visualization and statistical analysis to improve decision-making, optimize sales strategies, and enhance customer experience.

What is the difference between Retail Analytics vs Retail Data Analyst?

AspectRetail AnalyticsRetail Data Analyst
Required CredentialsBachelor's in Business, Data Science, or related fields; experience with analytics toolsBachelor's in Statistics, Data Analysis, or related fields; proficiency in data tools
Work EnvironmentCollaborates with marketing, sales, and operations teams to interpret dataAnalyzes sales and customer data to generate reports and insights
Employer & Industry UsageUsed by retail chains, e-commerce companies, and market research firmsCommonly employed within retail companies for data reporting and analysis

Retail Analytics focuses on interpreting large datasets to inform strategic decisions, often involving predictive modeling and advanced analytics. Retail Data Analysts primarily gather, process, and report on sales and customer data to support operational decisions. While both roles require similar skills and work environments, Retail Analytics tends to involve more strategic and predictive work, whereas Retail Data Analysts focus on data reporting and insights generation.

What cities in California are hiring for Retail Analytics jobs? Cities in California with the most Retail Analytics job openings:
Infographic showing various Retail Analytics job openings in California as of July 2026, with employment types broken down into 1% Internship, 91% Full Time, 5% Part Time, 1% Temporary, and 2% Contract. Highlights an 79% Physical, 5% Hybrid, and 16% Remote job distribution, with an average salary of $168,048 per year, or $80.8 per hour.

Manager of Analytics

Rodan and Fields Beauty, LLC

San Francisco, CA โ€ข On-site, Remote

Full-time

Re-posted 15 days ago


Job description

Job Description: Manager, Analytics (Retail, Brand, Innovation & Affiliate)

Role: Manager, Analytics
Department: Strategy & Analytics
Reports To: Senior Director, Strategy & Analytics
Location: US (Hybrid/Remote as applicable)

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Role Overview

Rodan + Fields is seeking a highly skilled Manager, Analytics to lead advanced analytics in support of Retail, Brand, Innovation and Affiliate channels. This role combines deep technical expertise, strong business judgment, and clear executive communication to translate complex data into actionable insights that inform commercial decisionโ€‘making.

The Manager will act as the primary analytics partner for assigned business areas, owning analytical problem framing, insight development, and performance storytelling, while partnering closely with the Senior Manager on enterprise standards, customer frameworks, and strategic priorities. This role serves as the primary owner of advanced analytical execution for its channels, including model development, experimentation, and ongoing iteration, while partnering with the Senior Manager for enterprise alignment and executive application.

This is a managerโ€‘level individual contributor role (no direct people management initially), designed for an analytically rigorous leader who can operate independently across complex problem spaces and communicate effectively with both technical and nonโ€‘technical audiences.

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Key Responsibilities

Advanced Analytics & Insight Leadership

  • Lead endโ€‘toโ€‘end analytical workstreams across Retail, Brand, Innovation and Affiliate channels
  • Partner with business stakeholders to frame hypotheses, define analytical approaches, and ensure analyses are grounded in clear business questions
  • Identify core performance drivers and synthesize insights that explain what happened, why it happened, and what to do next
  • Apply advanced analytical techniques to evaluate campaigns, promotions, launches, and ongoing channel performance
  • Own the development, iteration, and maintenance of advanced analytical models and experimentation frameworks for assigned channels

Marketing Measurement & Modeling

  • Develop and apply marketing measurement frameworks, including attribution and media mix modeling
  • Design and interpret experiments and quasiโ€‘experiments to assess incrementality and ROI
  • Build and apply machine learning models (e.g., churn prediction, probabilistic segmentation, response modeling) in applied business contexts

Data & Technical Execution (Handsโ€‘On)

  • Demonstrate expert proficiency in SQL, including complex querying across largeโ€‘scale data warehouses
  • Work fluently with cloudโ€‘based data environments, with preference for Google Cloud Platform (BigQuery)
  • Partner with analytics and data engineering resources to automate datasets, reporting layers, and analytical workflows
  • Oversee development and deployment of productionโ€‘ready dashboards and visualization products, with Power BI strongly preferred

Retail Analytics (Physical Stores & Omnichannel Impact)

  • Lead retail analytics focused on understanding the incremental impact of physical stores, including store-level and market-level incrementality analyses
  • Evaluate the role of physical retail within the broader omnichannel ecosystem, including halo effects, customer acquisition, and cross-channel cannibalization or lift
  • Partner with Retail and Finance stakeholders to assess store performance, test-and-learn results, and implications for expansion, optimization, or investment decisions

Innovation Analytics (Market Entry, Trial & Repeat)

  • Support Innovation analytics through market analysis, sizing, and performance assessment for new concepts, products, or business models
  • Analyze customer trial, repeat behavior, and early cohort performance to inform go/no-go, scaling, and iteration decisions
  • Evaluate potential cannibalization or substitution effects between new innovations and existing products or channels

Brand Analytics (Trial, Repeat & Brand Performance)

  • Partner with Brand stakeholders to analyze trial and repeat dynamics, customer adoption patterns, and brand performance over time
  • Support campaign and launch readouts with a focus on driving sustained customer engagement and repeat behavior, not just short-term lift

Affiliate Analytics (Brand Consultant Performance & Cohorts)

  • Lead Affiliate analytics focused on understanding Brand Consultant performance by cohort, including sales productivity and engagement
  • Evaluate whether Affiliate initiatives, programs, and activities are driving incremental performance and positive impact within targeted Brand Consultant cohorts
  • Develop and refine cohort frameworks to distinguish consultants who are actively building their business from those who are less engaged, and track movement between cohorts over time
  • Partner with Affiliate leadership to assess the effectiveness of incentives and engagement efforts, and translate findings into clear recommendations

Financial & Crossโ€‘Functional Partnership

  • Apply a strong understanding of financial concepts and workflows (e.g., operating margin, COGS, CAC, corporate planning) to analytical projects
  • Partner closely with Finance to ensure analytical outputs align with financial narratives and decision processes
  • Support collaborative analyses that connect customer and channel behavior to financial outcomes

Communication & Stakeholder Leadership

  • Translate complex quantitative findings into clear, compelling narratives for nonโ€‘technical audiences
  • Integrate insights across multiple data sources to develop cohesive, decision-ready narratives rather than isolated point analyses
  • Present insights and recommendations to senior leaders with confidence and clarity
  • Navigate ambiguous problem spaces, distill complexity into core impact drivers, and communicate tradeoffs effectively
  • Adapt communication style to stakeholders with varying levels of technical and quantitative fluency

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Qualifications

Experience

  • 7+ years of experience in analytics, data science, marketing analytics, or a related quantitative field
  • Advanced degrees (Masterโ€™s or PhD) in a quantitative discipline may substitute for some years of experience
  • Experience operating in consumer, retail, DTC, or omnichannel environments strongly preferred

Education

  • Bachelorโ€™s degree required in a quantitative or STEM discipline, such as:
    • Statistics
    • Operations Research
    • Data Science
    • Mathematics
    • Computer Science or other quantitatively rigorous STEM fields

Technical & Analytical Skills

  • Expertโ€‘level SQL proficiency, including work with largeโ€‘scale data warehouses and automated data workflows
  • Strong experience with cloud data platforms, preferably Google Cloud Platform
  • Proven expertise in machine learning and statistical modeling, including:
    • Churn and retention modeling
    • Predictive and probabilistic segmentation
    • Regression and classification techniques
  • Strong statistical foundation, including:
    • Hypothesis testing
    • Experimental design
    • Predictive modeling and inference
  • Demonstrated experience deploying and maintaining production analytics and dashboards, with Power BI preferred

Business & Communication Skills

  • Strong fluency in financial concepts and the ability to work effectively with Finance partners
  • Exceptional written and verbal communication skills
  • Demonstrated ability to extract insights from data and explain them plainly and persuasively to nonโ€‘quantitative audiences
  • Comfort presenting to executives and collaborating with stakeholders across varying levels of analytical sophistication
Salary Range: $91,200 - $148,000ย 
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The pay range represents the low and high end of the salary range we reasonably expect to pay for this position at the time of posting. We may ultimately pay more or less than the posted range, and the range may be modified in the future.ย An employeeโ€™s pay position within the salary range will be based on several factors including, but not limited to, to geographic location, experience, education, skills, qualifications, performance, and business or organizational needs. The range listed is just one component of Rodan + Fieldsโ€™ total compensation package for employees.ย ย