Associate Data Scientist – Finance & Accounting Automation

Qualcomm•San Diego, CA

About The Position

This position is not eligible for Qualcomm immigration sponsorship. We care deeply about transforming lives with Qualcomm technology to enrich our industry, our communities, and the world. Our mission is to build great products that accelerate next-generation computing experiences — the building blocks for the data center, artificial intelligence, PCs, gaming, and embedded systems. Underpinning our mission is the Qualcomm culture. We push the limits of innovation to solve the world’s most important challenges. We strive for execution excellence while being direct, humble, collaborative, and inclusive of diverse perspectives. At Qualcomm: Together, we advance. We are looking for a visionary data scientist to lead the transformation of finance and accounting operations through intelligent automation. This role combines deep technical expertise with strong business acumen to simplify processes, reduce manual effort, and accelerate AI adoption across the organization. You will design and deploy AI systems—including AI agents, and Model Context Protocols (MCP)—that automate complex workflows and empower teams through self-service tools, training and extensive change management.

Requirements

Bachelor's degree in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, or related field.
Bachelor’s or Master’s degree (MS strongly preferred) in Finance, Accounting, Economics, Computer Science, AI/ML, Applied Mathematics, or related field.
1 years of experience building and deploying AI/ML models in enterprise environments.
Proficiency in Python or Rust; experience with frameworks like PyTorch, TensorFlow, LangChain, or similar.
Experience in front-end UI development, with proficiency in React or similar modern frameworks (e.g., Vue.js, Angular, Svelte), including building responsive, user-friendly web applications.
AI automation and workflow development using N8N or similar tools.
Deep understanding of LLMs, AI agents, and context-aware orchestration (e.g., MCP).
Strong business acumen in finance and accounting, with the ability to map technical solutions to operational goals.
Experience with ERP systems (e.g., Oracle, SAP) and financial data structures.
Proficiency in Tableau, Power BI, or similar tools for self-service reporting and visualization.
Excellent communication and stakeholder management skills.

Responsibilities

Partner with finance and accounting teams to deeply understand workflows and pain points.
Translate business needs into technical solutions that simplify and streamline operations.
Ensure all solutions align with compliance standards and audit requirements.
Build and deploy AI/ML business solutions (LLMs, agents) and workflows for various finance and accounting tasks such as journal entry automation, reconciliation, forecasting, and other similar use-cases.
Design and develop interactive user interface to integrate user-feedback with AI workflows leveraging React (preferred) or similar platforms.
Integrate the solution workflow into existing enterprise systems such as Oracle ERP, SAP, Price Management, TM1, and Salesforce.
Develop robust data pipelines in Python to extract, clean, and prepare data from enterprise platforms (Databrics, AWS Redshift, Oracle etc) for AI, and reporting use cases.
Drive AI and automation adoption by creating training programs and documentation for finance stakeholders.
Promote the use of self-service AI and automation tools to empower non-technical users.
Drive adoption through development of intuitive and simplified user interface with deep integration to AI solutions.
Collaborate with IT and operations to leverage and integrate enterprise AI tools into existing systems and processes.
Build and maintain data pipelines that support real-time and batch AI applications.
Leverage Tableau, Power BI, and other self-service reporting tools to visualize insights and track automation impact.
Define KPIs and success metrics for AI initiatives in finance.
Stay current with advancements in generative AI, agent-based modeling, and enterprise AI governance.
Present findings and proposals to executive stakeholders and steering committees.