Sr Principal Data Scientist, Search and Recommendations

Ingram Micro, Inc.; Irvine (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

A global technology firm is seeking an experienced Data Scientist / Senior Consultant to drive the strategy and execution of their search and recommendation systems. The ideal candidate will possess a Master’s or Ph.D. in a quantitative field and over a decade of relevant experience, particularly in search and recommendation systems at scale. This is a leadership role that requires technical expertise and the ability to communicate complex ideas effectively. Ideal for those passionate about data science and its impact on business outcomes.

Qualifications

• 10+ years functional experience in search and recommendation systems.
• Track record of managing production search systems with measurable impact.
• Experience conducting A/B tests tied to business metrics.

Responsibilities

• Own technical development and roadmap for search and recommendation systems.
• Accountable for model quality and production impact.
• Influence platform and architecture decisions.

Skills

Expertise in developing search algorithms

Ability to translate business problems into AI solutions

Leadership and mentorship abilities

Education

Master’s or Ph.D. in a quantitative field

Tools

Python

SQL

Jupyter

R

GCP (Big Query, VertexA, Gemini)

Job description

We are seeking an experienced Data Scientist / Senior Consultant to drive the science and strategy behind Ingram Micro’s global Search and Recommendation systems. The role partners closely with a diverse team of scientists, engineers, product leaders, and industry experts to build large‑scale models that meaningfully improve discovery, relevance, and commercial outcomes across an enterprise ecosystem.

You will be responsible for the technical strategy, model development, experimentation, and production impact, delivering measurable improvements to the performance of search and recommendation engines worldwide. The role is hands‑on and high‑visible requiring deep technical expertise and the ability to communicate complex concepts to non‑technical audiences.

Scope

• Own multi-quarter technical development, vision, and roadmap for search, ranking, personalization, discovery, and recommendation systems.
• Engage with a globally distributed team of Data Scientists and AI/ML Engineers
• Be accountable for model quality, experimentation rigor, and production impact
• Influence platform, tooling, and architecture decisions in partnership with science, engineering, and business leadership

Qualifications

• Master’s or Ph.D. in a quantitative field (Computer Science, Data Science, AI, Statistics, Applied Math, Engineering, Economics, or similar technical fields)
• 10+ years functional experience including a minimum of 5+ years position specific experience in search, recommendation, information retrieval, or personalization at scale
• Track record of owning production search or recommendation systems with measurable business impact

Hands‑On Technical Ownership

• Expertise in developing and leading the deployment of enterprise level Search Algorithms and Recommendation Engines at global scale
• Develop model design for ranking, relevance, personalization, semantic retrieval
• Build applications leveraging LLMs, deep learning, transformers, generative AI, semantic search, vector databases, and RAG pipelines
• Design and interpret A/B tests and quasi‑experiments tied to business metrics
• Tools/stack experience: Python, SQL, Jupyter, R, Julia, SAS, MATLAB, etc…
• Experience with GCP (Big Query, VertexA, Gemini) strongly preferred
• Experience developing Search/Recommendation Agents is a plus
• Work with large‑scale behavioral, content, and transactional datasets to solve highly ambiguous problems

Organizational & Technical Leadership

• Partner with and mentor mid- and senior -level scientists
• Set bar for modeling excellence, experimentation discipline, production readiness
• Promote reusable ML platforms and standardization over one‑off solutions
• Serve as an executive-facing authority on search and recommendation science

Stakeholder & Business Leadership

• Translate ambiguous business problems into scalable AI and ML solutions
• Communicate technical models, engines, systems to non-technical stakeholders
• Partner with Product, Engineering, and Business to balance near-term wins vs. long-term platform bets

Success Metrics

• Sustained improvements in relevance, engagement, and conversion
• High-retention, high-performing ML team with strong internal mobility
• Systems that scale globally with reliability and defensible scientific rigor
• Measurable impact to financial results and customer experience