Senior Data Science & ML Platform Specialist

Role Overview: You will be joining a fast-growing consumer products company in Los Angeles, CA as a Full-Stack Data Scientist / ML Engineer. Your primary responsibility will be to own internal data infrastructure, AI automation, knowledge systems, and workflow optimization. This role will have a direct impact across various departments such as operations, sales, customer support, and product development. Reporting directly to the CEO, you will have high visibility and autonomy in this position. Key Responsibilities: - AI & Automation: - Build and maintain internal AI agents supporting quoting, product lookup, workflow automation, and exception handling. - Develop NLP systems to automatically parse customer inquiries, classify account types, and identify action routing. - Maintain and expand an internal LLM-powered knowledge base capturing business rules, supplier constraints, SKU attributes, and operational exceptions. - Data Engineering & ETL Pipelines: - Design and manage production-grade ETL pipelines across CRM, accounting, supplier data, SKU catalogs, and operational datasets. - Create reliable data models unifying siloed systems for high-quality analytics and automation. - Implement automated data-cleaning processes supporting daily operations and decision-making. - Internal Tools & Script Development: - Develop Python tools and micro-automations for pricing logic, invoice workflows, sample handling, customer communication, and data synchronization. - Create internal dashboards, utilities, and command-line workflows supporting sales, operations, and leadership. - Act as the primary builder of internal automation infrastructure. - Operations & Process Analytics: - Analyze operational bottlenecks and performance trends across production timelines, shipping, and customer response cycles. - Provide insights directly improving efficiency, speed, and customer experience. - Cross-Functional Collaboration: - Collaborate closely with leadership and operations teams to identify automation opportunities. - Translate evolving business rules into robust data logic and system behavior. - Document internal processes and maintain reliable technical infrastructure. Qualifications Required: - 36+ years in Data Science, ML Engineering, or a full-stack data role. - Strong Python engineering fundamentals (Pandas, NumPy, scikit-learn, async workflows). - Hands-on experience with LLM systems (embeddings, retrieval, fine-tuning, structured prompting, agent-style workflows). - Experience building and maintaining ETL pipelines from multiple SaaS systems and CSV/Excel sources. - Experience integrating with CRM and accounting APIs (HubSpot, QuickBooks, or comparable platforms). - Strong SQL skills and experience designing logical data models. - Demonstrated ability to build internal automation tools or operational scripts. - Excellent written communication and documentation skills. Company Additional Details: Omit this section as there are no additional details of the company mentioned in the job description. Role Overview: You will be joining a fast-growing consumer products company in Los Angeles, CA as a Full-Stack Data Scientist / ML Engineer. Your primary responsibility will be to own internal data infrastructure, AI automation, knowledge systems, and workflow optimization. This role will have a direct impact across various departments such as operations, sales, customer support, and product development. Reporting directly to the CEO, you will have high visibility and autonomy in this position. Key Responsibilities: - AI & Automation: - Build and maintain internal AI agents supporting quoting, product lookup, workflow automation, and exception handling. - Develop NLP systems to automatically parse customer inquiries, classify account types, and identify action routing. - Maintain and expand an internal LLM-powered knowledge base capturing business rules, supplier constraints, SKU attributes, and operational exceptions. - Data Engineering & ETL Pipelines: - Design and manage production-grade ETL pipelines across CRM, accounting, supplier data, SKU catalogs, and operational datasets. - Create reliable data models unifying siloed systems for high-quality analytics and automation. - Implement automated data-cleaning processes supporting daily operations and decision-making. - Internal Tools & Script Development: - Develop Python tools and micro-automations for pricing logic, invoice workflows, sample handling, customer communication, and data synchronization. - Create internal dashboards, utilities, and command-line workflows supporting sales, operations, and leadership. - Act as the primary builder of internal automation infrastructure. - Operations & Process Analytics: - Analyze operational bottlenecks and performance trends across production timelines, shipping, and customer response cycles. - Provide insights directly improving efficiency, speed, and customer exp