Applied AI Data Scientist for Legal Tech & Production ML

DLA Piper; Los Angeles (CA); Hybrid; USD 130,000 - 206,000; Full time

Benefits offered by this job

Hybrid work arrangement

Job summary

DLA Piper is seeking a Data Scientist for Applied AI Solutions in the United States on a hybrid schedule. The role focuses on collecting and analyzing large datasets to improve business and client outcomes, deploying ML models, and translating regulatory needs into analytic methods.

You will work with lawyers and clients to deliver data-driven solutions with measurable impact, including bias testing and model governance across enterprise platforms.

Qualifications

• Bachelor's degree in computer science, data science, statistics, applied mathematics or related field.
• 5 years of relevant experience applying data science and machine learning techniques to complex problems.
• Strong coding experience in Python and SQL, building and deploying production models at scale.
• Ability to translate business and regulatory requirements into analytic objectives and testable criteria.

Responsibilities

• Develops and applies algorithmic testing methods to evaluate AI systems, models, and tools for bias, accuracy, reliability, robustness, and performance.
• Creates novel testing techniques, metrics, and evaluation frameworks for emerging AI systems and risks when established methods are insufficient.
• Translates legal, regulatory, and business requirements into analytic objectives, testable criteria, data specifications, and practical technical solutions.
• Works with internal stakeholders, lawyers, and clients to identify opportunities to leverage organizational and client data to address business, legal, investigative, and regulatory needs.
• Prioritizes, scopes, and manages multiple concurrent data science projects and client engagements, with accountability for delivery quality, timelines, measurable business outcomes, and commercially valuable solutions.
• Designs, builds, deploys, and supports production machine learning models and data science solutions at scale, including solutions for privilege review and related legal workflows.
• Designs representative test datasets, test cases, benchmarks, validation protocols, and monitoring approaches; documents methodologies, results, limitations, and recommendations.
• Assesses the quality, completeness, representativeness, and accuracy of structured and unstructured data sources and data-gathering techniques.
• Uses Python and SQL extensively to build models, data pipelines, testing tools, and analyses.
• Manages structured and unstructured datasets in secure enterprise environments and works with Azure AI services or comparable enterprise cloud and data platforms.
• Develops custom data models, algorithms, and predictive solutions that improve client outcomes and support data-driven decision-making.
• Coordinates with legal, technical, and business teams to implement solutions, monitor outcomes, and refine models and testing methods.
• Communicates methods, findings, risks, and actionable insights to technical and non-technical audiences through client-ready analyses, documentation, and presentations.
• Establishes and applies best practices for reproducible analysis, model governance, quality assurance, data protection, and reporting.
• Supports the delivery of revenue-generating client solutions within consulting or professional-services delivery models.

Skills

Python

SQL

Machine Learning

Statistical Modeling

Data Visualization

Big Data

Communication

Education

Bachelor's degree in computer science or related field

Tools

Azure AI services

Jupyter

Excel

Tableau

Power BI

SQL

Job description

DLA Piper is seeking a Data Scientist for Applied AI Solutions in the United States on a hybrid schedule. The role focuses on collecting and analyzing large datasets to improve business and client outcomes, deploying ML models, and translating regulatory needs into analytic methods.

You will work with lawyers and clients to deliver data-driven solutions with measurable impact, including bias testing and model governance across enterprise platforms.