Designs, develops, tests, and maintains complex data workflows and pipelines incorporating traditional data engineering, automation, and AI-enabled processing techniques.

Develops Python-based solutions for data processing, extraction, classification, transformation, validation, reconciliation, and reporting.

Designs workflows that use generative AI, large language models, multimodal AI, or other AI technologies to extract, classify, summarize, normalize, or structure information from PDFs, documents, images, spreadsheets, and other structured and unstructured data sources.

Develops structured AI workflows using techniques such as prompt engineering, schema-based outputs, JSON processing, validation logic, exception handling, and automated quality-control procedures.

Integrates AI capabilities with Python, APIs, databases, cloud services, and traditional data-processing workflows to create repeatable and scalable analytical solutions.

Evaluates AI-generated outputs for accuracy, completeness, consistency, and reliability and develop validation procedures appropriate for legal and client-facing work.

Designs human-in-the-loop review processes and other quality-control mechanisms for AI-assisted workflows where appropriate.

Identifies appropriate and inappropriate use cases for AI based on data sensitivity, analytical risk, accuracy requirements, confidentiality, and the intended use of the resulting work product.

Assists in developing standards, documentation, and governance practices for the responsible use of AI within Legal Data Analytics workflows.

Evaluates emerging AI technologies, models, platforms, and development approaches and recommend tools that can improve the efficiency, accuracy, or scalability of the department's analytical services.

Prototypes and developsAI-enabled tools and self-service applications that automate repetitive data preparation, extraction, review, or analytical processes.

Collaborates with attorneys and technical professionals to translate legal and business requirements into data engineering and AI-assisted analytical solutions.

Provides technical guidance to team members on data extraction, transformation, automation, reporting, data validation, and analytics tools and processes.

Strong attention to detail, organizational skills, and commitment to data accuracy and quality assurance.

Strong collaboration and client service orientation with the ability to partner effectively across departments and business functions.

Strong analytical, problem-solving, and critical thinking skills with the ability to work with complex datasets and identify data quality issues, trends, and anomalies.

Strong Python programming skills with demonstrated experience developing reusable data-processing, automation, and AI-enabled workflows.

Practical experience incorporating generative AI or large language models into data-processing or analytical workflows.

Understanding of prompt engineering and techniques for producing reliable, structured outputs from generative AI systems.

Experience working with structured AI outputs, including JSON schemas, parsing, validation, exception handling, and downstream data transformation.

Familiarity with multimodal AI techniques for processing documents, PDFs, images, or other unstructured information.

Ability to evaluate AI-generated results critically and design validation and quality-control procedures to identify hallucinations, omissions, inconsistencies, and other output errors.

Understanding of human-in-the-loop design and the importance of maintaining appropriate human review for higher-risk legal and analytical applications.

Ability to determine when traditional programming, deterministic rules, statistical methods, or AI-based approaches are most appropriate for a particular analytical problem.

Familiarity with responsible AI concepts, including data privacy, confidentiality, security, transparency, reproducibility, and appropriate use of AI within professional services environments.

Ability to prototype new AI-enabled analytical workflows and move successful concepts toward repeatable production processes.

Bachelor's degree in Data Analytics, Data Science, Computer Science, Information Systems, Statistics, Mathematics, Engineering, or a related quantitative or technical field required; equivalent combination of certifications and relevant professional experience may be considered

Minimum of 5 years of progressively responsible experience in data engineering, data analytics, database development, automation, business intelligence, or a related technical discipline.

Demonstrated Data extraction and document-processing techniques

Demonstrated Data validation, reconciliation, and automated quality control

Demonstrated experience using Python and SQL to develop complex data-processing and automation solutions.

Demonstrated experience incorporating artificial intelligence, generative AI, machine learning, natural language processing, or related technologies into data or document-processing workflows.

Experience independently developing solutions involving structured and unstructured data, including spreadsheets, databases, PDFs, documents, images, APIs, or other source formats.

Experience evaluating and validating automated or AI-generated outputs in environments where accuracy and reproducibility are important.

Experience supporting data-intensive environments, preferably within a law firm, legal services, professional services, consulting, compliance, human resources, financial, litigation, or employment analytics setting.

Experience mentoring, reviewing, or providing technical guidance to less experienced technical professionals preferred.

Development of AI-enabled self-service applications or analytical tools

Advanced Microsoft SQL Server and T-SQL

Python

Microsoft Excel

Generative AI / Large Language Models

Prompt engineering

REST APIs and JSON

Structured-output and schema-based AI workflows

Git or comparable source-control practices

Microsoft Azure

Microsoft Fabric

Azure Data Factory

Azure SQL Database

Azure AI services

Azure OpenAI or comparable enterprise LLM platforms

Power BI

SQL Server Integration Services (SSIS)

Multimodal AI and document intelligence technologies

Retrieval-augmented generation (RAG) concepts

Vector search or semantic search concepts

AI orchestration or agent-based workflow concepts

Machine learning or natural language processing

While performing the duties of this job, the employee is occasionally required to move from workstation or desk throughout the work area to work independently or with a team to meet with colleagues or supervisor and retrieve work assignments

This position may also be sedentary and require the employee to sit for extended periods of time

Requires manual dexterity to dial a telephone, enter data into a computer, handle objects, and operate tools