Senior Data Analyst

Skims•Los Angeles, CA

20h•Onsite

About The Position

SKIMS is a solutions-oriented brand creating the next generation of underwear, loungewear, shapewear, and essentials. We are setting new standards by delivering technically innovative, comfort-driven products with elevated design for everybody. At the intersection of culture, innovation, and commerce, SKIMS is a global brand redefining modern apparel. Our teams are built on creativity, collaboration, and operational excellence, and we are committed to fostering an inclusive, high-performance environment where ambitious, curious, and values-driven individuals can do the best work of their careers. As we continue to scale globally, our HQ teams play a critical role in shaping strategy, building world-class infrastructure, and driving sustainable growth across markets. We are looking for a Senior Data Analyst based in Los Angeles to join our Data & Analytics team. In this role, you will partner closely with a key business function (areas could include Performance Marketing, Product Planning, Merchandising, Operations, or Production) with the final placement informed by your background and where your expertise will have the greatest impact. You will work as part of a collaborative data team while serving as the analytical point of contact for your stakeholders, translating complex data into clear, actionable insights that drive real decisions.

Requirements

Bachelor’s degree in Statistics, Data Science, or a related field and 5+ years of experience in a senior analytics role.
Accessing, transforming, and modeling large datasets using SQL and Python to surface patterns, trends, and actionable insights, including experience applying predictive and statistical models to business problems.
Querying and manipulating large datasets using BI and analytics tools (e.g., Snowflake, SQL, Python, Looker), with strong command of data wrangling, transformation, and exploratory analysis.
Designing and measuring experiments, including A/B tests, to evaluate business initiatives.
Creating executive-level dashboards using visualization tools like Looker, Tableau, or PowerBI.
Leading end-to-end analytics projects with minimal oversight, defining measurement frameworks and translating ambiguous business questions into structured analytical plans.

Nice To Haves

Prior experience in the ecommerce or retail industry (e.g., DTC apparel, fashion, or consumer goods) is a plus.

Responsibilities

Partner with a dedicated business function as the primary analytics contact, owning analytical deliverables and translating business questions into clear, data-driven recommendations.
Build and maintain reusable analytical frameworks, scripts, and tools in Python or SQL to automate recurring analyses, improve data access, and scale the team’s capabilities.
Apply statistical and predictive modeling techniques to uncover patterns, forecast outcomes, and support decisions around areas such as customer lifetime value, demand planning, and resource allocation.
Identify opportunities for optimization, customer segmentation, and targeting across the supported business function by providing stakeholders with actionable insight into customer preferences, behaviors, and trends.
Design and execute experiments, including A/B tests, to measure the impact of business initiatives and communicate results and recommendations clearly to stakeholders.
Evaluate and validate analytical models using appropriate statistical performance metrics to ensure accuracy and reliability.
Develop analytics roadmaps in partnership with business stakeholders, aligning data initiatives to functional goals and translating trend analysis into actionable forecasts.
Partner with data engineering and technology teams to define data requirements, ensure data quality, and build robust pipelines that support reliable, scalable analytics across the organization.
Create executive-level dashboards using visualization tools like Looker, Tableau, or PowerBI that empower self-service, data-driven decision making.
Build strong relationships with business stakeholders, proactively identifying analytical needs, communicating findings in clear and compelling ways, and influencing decisions at the leadership level.
Serve as a resource and informal mentor to junior team members, sharing expertise in analytical methods, data best practices, and stakeholder communication to continuously elevate the team’s capabilities.