Senior Manager, AI & Data Science Engineer

Andersen•Los Angeles, CA

17h•Onsite

About The Position

We are looking for a Senior Manager, AI & Data Science Engineer to join our internal National Tax Technology group. This team is dedicated to building analytical tools, data infrastructure, and AI-powered applications to enhance the tax practice. The role involves working at the intersection of machine learning, data engineering, and generative AI, translating complex tax workflows into scalable technology solutions for thousands of tax professionals. This is a high-impact, full-stack data science role where you will own end-to-end development, from setting up pipelines and processing data to deploying models and LLM-powered applications. You will collaborate with tax subject matter experts, IT, and product stakeholders to understand problems and build effective solutions.

Requirements

5-7 years of experience in a data science, machine learning engineering, or data engineering role
BA in Computer Science, Data Science or Statistics
Proficiency in Python (pandas, scikit-learn, PyTorch or TensorFlow) and SQL
Hands-on experience deploying ML models to production (MLflow, SageMaker, Vertex AI, or equivalent)
Experience building or integrating LLM-based applications (Claude, PLTR, OpenAI API, MS Azure OpenAI, LangChain, or similar)
Experience with cloud data platforms and pipeline orchestration (Airflow, dbt, or equivalent)
Strong communication skills — you can explain a model's behavior to a tax partner who has never heard of gradient descent
Applicants must be currently authorized to work in the United States on a full-time basis upon hire. Andersen will not consider candidates for this position who require sponsorship for employment visa status now or in the future (e.g., H-1B status).

Nice To Haves

Master’s degree preferred
Exposure to tax, finance, or accounting data (ERP systems, ONESOURCE, Corptax, or similar platforms)
Experience with vector databases and RAG architectures (Pinecone, Weaviate, pgvector)
Familiarity with document processing pipelines (OCR, PDF extraction, entity recognition on financial documents)
Knowledge of data governance and auditability requirements in regulated industries

Responsibilities

Design, train, and evaluate ML/AI models and workflows for tax use cases such as risk classification, anomaly detection, and predictive compliance analytics.
Build and maintain model pipelines from feature engineering through deployment, monitoring, and retraining.
Work with tax domain experts to translate regulatory and process knowledge into model features and evaluation criteria.
Develop LLM-powered tools for tax research, document review, and summarization using retrieval-augmented generation (RAG) and prompt engineering techniques.
Evaluate and fine-tune foundation models for domain-specific tax and regulatory language.
Build safeguards and evaluation frameworks to ensure accuracy and auditability in AI-generated outputs.
Design and maintain data pipelines that ingest, transform, and serve structured tax data (ERP outputs, trial balances, return data) and unstructured data (regulatory documents, correspondence).
Build and maintain data models that serve both ML/AI workloads and downstream analytics.
Ensure data governance, lineage, and quality standards are met.
Design and deploy systems engineering and data integration architecture and tools (APIs, etc.).
Build dashboards and analytical tools that give tax teams visibility into workflow performance, compliance metrics, and process efficiency.
Conduct ad hoc quantitative analyses to support tax leadership decisions and technology investment prioritization.