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About This Role
The pay range for this role is $93,000 \- $130,000/yr USD.WHO WE ARE:
Headquartered in Southern California, Skechers—the Comfort Technology Company®—has spent over 30 years helping men, women, and kids everywhere look and feel good. Comfort innovation is at the core of everything we do, driving the development of stylish, high\-quality products at a great value. From our diverse footwear collections to our expanding range of apparel and accessories, Skechers is a complete lifestyle brand.
ABOUT THE ROLE:
The Data Scientist will join our Data and Analytics team, reporting to Sr. Director, BI and Reporting. This role will focus on building advanced analytics solutions utilizing data science, AI and ML solutions and technology, building the intelligence that powers our core data products. The Data Scientist will be a hybrid professional who combines deep statistical rigor with an engineering first mindset, with a mission to translate complex business challenges into an end\-to\-end machine learning solution that can thrive in production. They will build, test and document new or improved data science applications and machine learning techniques, develop innovative analytic reports, maintain best practices for change management and collaborate with business leads, data stewards and power users to creatively solve real\-life business problems utilizing large\-scale data supporting all areas of our business.
WHAT YOU’LL DO:
- End to end ML development: Evaluate, implement, and improve machine learning techniques to solve real world problems and aid business decision making.
- Contribute extensively to the full model development lifecycle, from ideation, analysis, model creation and operation.
- Leverage large language models (LLMs) and RAG (retrieval augmented generation) frameworks to enhance data product reach and automate internal workflows.
- Write production\-grade, clean, maintainable, and scalable code for running experiments and proofs\-of\-concept. Collaborate with Data Engineers to build robust data pipelines and deploy models.
- Monitor model drift and bias in production, ensuring our AI solutions remain ethical, accurate and high performing over time.
- Lead Experimental Design, A/B testing and causal inference projects to measure the direct impact of product changes and model performance.
- Translate model outputs into actionable business strategies for non\-technical leadership, utilizing storytelling as a vehicle to make analytics \& insight deliverables accessible and memorable.
- Perform ad hoc analysis to uncover business insights and opportunities, exploring data to discover patterns, meaningful relationships, anomalies, and trends across different formats and platforms.
- Employ quick prototyping to gather feedback and adjust to user asks in an agile fashion.
- Follow best practices to build scalable enterprise BI and analytics solutions, ensuring data and reports are thoroughly tested before production release.
- Document objectives and solutions in summary as well as technical details.
- Collaborate with data and BI engineers, data product managers and business users to explore and create solutions for relevant business problems.
WHAT YOU’LL BRING:
- Proficiency with programming and data analysis using Python, Spark, and ML frameworks (NumPy, Pandas, Sci\-kit Learn, XGBoost, TensorFlow, PyTorch).
- Strong mathematical foundation in statistics, probability, optimization algorithms, linear algebra and AI technologies.
- Working knowledge of statistical and machine learning techniques including classification, regression, clustering, and multivariate methods.
- Expertise with relational data modeling and advanced SQL for data manipulation and performance optimization.
- Experience working in an Agile/SCRUM environment
- Strong problem\-solving, analytical, and communication skills (written, verbal, and interpersonal).
- Strong organizational skills, attention to detail, and ability to prioritize workload.
- Professional presence with ability to take initiative, be creative, curious, and collaborative in a flexible, team\-oriented environment.
- Ability to independently conduct in\-depth data analysis.
REQUIREMENTS:
- Bachelor's/Master's/Ph.D. in quantitative field (Computer Science, Statistics, Mathematics, Physics) or equivalent industry experience.
- 3\+ years in a professional data science role with proven track record of moving models from research to production.
- 5\+ years experience in professional data science role with advanced analytics applications preferred.
- Strong communication and presentation skills – ability to articulate technical challenges and solutions to diverse audiences.
- Familiarity with retail, supply chain, digital \& financial analysis, predictive analytics, and BI visualization toolsets.
- Experience with DSML Automation platforms and MLOps tools.
- Experience with Data Engineering/Cloud tools (Snowflake, BigQuery, AWS SageMaker), GenAI tools (Hugging Face, LangChain, LlamaIndex, OpenAI/Gemini APIs), and cloud\-based BI and Analytics technologies.
- Ability to work with minimal oversight while ensuring timely, accurate task completion.
About Skechers
Skechers, a global Fortune 500® company, develops and markets a diverse range of lifestyle and performance footwear, apparel, and accessories. Serving over 180 countries and territories, Skechers connects customers to products through department and specialty stores, e\-commerce and digital stores, and through our more than 5,300 Skechers retail locations.
Equal Employment Opportunity
Skechers is committed to providing a safe, inclusive, and respectful work environment. Skechers provides equal employment opportunities for all employees and applicants for employment without regard race, color, religion, gender, gender identification and expression, national origin, marital status, age, disability, genetic information, military status, sexual orientation, or any other protected characteristic established by local, state or federal law.
Reasonable Accommodation
Applicants for employment who require a reasonable accommodation to apply for a job should request appropriate accommodation by emailing [email protected].
To perform this job successfully, an individual must be able to perform each job responsibility satisfactorily. The skills, abilities and physical demands described are representative of those duties that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodation may be made to enable individuals with disabilities, who are otherwise qualified for the job position, to perform the essential functions.
Salary Context
This $93K-$130K range is in the lower quartile for Data Scientist roles in our dataset (median: $162K across 211 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,824 AI roles we're tracking, Data Scientist positions make up 7% of the market. At Skechers, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $200,000 based on 697 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($111K) sits 44% below the category median. Disclosed range: $93K to $130K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Skechers AI Hiring
Skechers has 1 open AI role right now. They're hiring across Data Scientist. Based in Hermosa Beach, CA, US. Compensation range: $130K - $130K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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