Job Location: Multiple (Mexico, Brazil, Bolivia, Chile, Colombia, Peru)
Calling all originals: At Levi Strauss & Co., you can be yourself — and be part of something bigger. We’re a company of people who like to forge our own path and leave the world better than we found it. Who believe that what makes us different makes us stronger. So add your voice. Make an impact. Find your fit — and your future.
At Levi Strauss & Co, we are revolutionizing the apparel business and redefining the way denim is made.
We are taking one of the world's most iconic brands into the next century: from creating machine learning-powered denim finishes to using block-chain for our factory workers' wellbeing, to building algorithms to better meet the needs of our consumers and optimize our supply chain.
Be a pioneer in the fashion industry by joining our global Data, Analytics & AI "startup with assets," where you will have the chance to build exciting solutions that will impact our global business across continents.
As a Senior Data Scientist you will be a technical leader within the global Data, Analytics and AI team, driving the design and delivery of advanced analytics, machine learning, and AI solutions that address our consumers' needs while streamlining our processes and approaching the business in more innovative ways. This role will focus on applications of Data, Analytics, and AI to DTC. Potential areas of application of AI include CLTV, CRM optimization, Segmentation, Recommendations, Personalization and Agentic implementations among others.
In this role, you will own the technical direction of key workstreams, influence the team’s roadmap, and serve as a bridge between data science and business stakeholders across regions. You will drive innovation, machine learning operationalization, adoption of data products, and data visualization / user interfaces / storytelling that put data at the center of our business. This role will aim to drive revenue and reduce cost through cross-region implementation, automation and business process improvement.
You will be expected to raise the technical bar across the team: mentoring Data Scientists, establishing best practices, conducting code and model reviews, and contributing to the global Data, Analytics & AI community by sharing your knowledge and leveraging other colleagues’ experience in designing scalable, global solutions.
About the Job
Own and drive the technical direction of multiple roadmap workstreams, from problem framing through production deployment, partnering with teams across the global organization.
Lead the design of complex ML/AI solutions using advanced ML, deep learning, and generative AI techniques to solve high-impact retail and consumer problems.
Act as the primary technical point of contact for business stakeholders on assigned workstreams, translating business problems into data science solutions and communicating results to non-technical audiences.
Mentor and coach Data Scientist 1 and Data Scientist 2 team members, providing technical guidance on modeling approaches, code quality, and career development.
Conduct advanced statistical analysis to provide applicable insights, identify trends, and measure performance.
Build complex predictive models using ML, DL, and GenAI techniques with production-quality code and jointly own complex data science workflows with the Data Engineering and ML Engineering teams.
Define and enforce best practices for model development, testing, documentation, and deployment within the team.
Scan the Data Science landscape for recent developments and proactively identify opportunities to integrate new methodologies into the existing project portfolio.
Contribute to architectural decisions on the team’s ML/AI infrastructure, including model serving, feature stores, and observability.
Research best practices, conduct experiments, and build relationships with industry leaders and technology partners (e.g., Google, cloud vendors).
Ensure that the risks associated with projects are raised and mitigated to keep all projects on track.
Represent the Data Science team in cross-functional forums, technical reviews, and vendor evaluations.
About You
Required Technical Skills
5+ years of hands-on experience analyzing data and developing supervised and unsupervised models; including variable selection, feature engineering, model generation, model diagnostics, interpretability, and deployment.
5+ years of experience writing complex SQL queries and working with large datasets in cloud/Hadoop-based environments. Experience writing BigQuery and dbt jobs.
5+ years of experience with data visualization tools (D3.js, R Shiny, Looker, Tableau, or similar); able to determine the appropriate visualization for a variety of data types and create compelling stories with data.
5+ years of experience deploying Machine Learning algorithms in a production environment.
3+ years of experience developing Deep Learning based solutions for a production environment.
Experience with Generative AI and LLMs — prompt engineering, RAG architectures, fine-tuning, and/or agentic AI frameworks (e.g., LangChain, Google ADK, or similar).
Experience working with different cloud services to develop ML solutions with special focus on Google Cloud Platform (Vertex AI, BigQuery ML, Cloud Run).
Expert-level Python, R, or similar programming skills.
Demonstrated experience mentoring junior and mid-level data scientists.
Experience with experiment design and causal inference methods (A/B testing, uplift modeling, difference-in-differences).
Experience with distributed systems (Docker, Kubernetes, Kafka, or Spark).
Experience in Reinforcement Learning is a plus.
Desired Education and Experience
Master’s/Ph.D. degree (computer science, applied mathematics, applied statistics, data science, or related technical discipline).
Overall 5+ years (with Master’s) or 3+ years (for Ph.D.s) of professional experience in Data Science, Machine Learning, Mathematics, Applied Statistics, or Operational Research in the tech or retail industry.
Track record of delivering end-to-end ML solutions that drove measurable business impact (revenue, cost savings, or efficiency gains).
Prior experience in a senior or lead data scientist capacity, with evidence of influencing team practices and stakeholder decisions.