Data Scientist

Skechers•Manhattan Beach, CA

40d•$93,000 - $130,000

About The Position

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.

Requirements

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.
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.

Nice To Haves

5+ years experience in professional data science role with advanced analytics applications.
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.

Responsibilities

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.