Applied AI Solutions Data Scientist — Hybrid

DLA Piper LLP (US); United States; Hybrid; USD 130,000 - 206,000; Full time

Benefits offered by this job

Hybrid work arrangement

Competitive compensation

Job summary

DLA Piper LLP (US) is seeking a Data Scientist in Applied AI Solutions to design, deploy, and scale data models and ML solutions that improve client outcomes. You will translate legal and regulatory needs into analytic objectives, develop testing frameworks, and collaborate with lawyers and clients to deliver measurable business impact.

The role emphasizes bias/accuracy testing, working with cloud platforms like Azure, and building end-to-end data pipelines using Python and SQL in a hybrid U.S.

Qualifications

• Experience in a law firm or other legal-services environment is preferred.
• Proven experience delivering revenue-generating client solutions with measurable outcomes, managing multiple concurrent client engagements, and operating within consulting or professional-services delivery models.
• A specialization in machine-learning, artificial intelligence, cognitive science or data science is preferred.
• Must be self-driven, curious and creative.
• Experience with advanced machine learning algorithms, statistics, and data mining techniques.
• Experience with the listed data science tools and platforms (Python, R, SQL, SAS, NLTK, Scikit-Learn, Excel, Tableau, Power BI, Jupyter; Hadoop, Azure, etc.)
• Must be adept in agile methodologies and machine learning pipelines.

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.
• Asseesses data quality and sources; evaluates structured and unstructured data and data-gathering techniques.
• Uses Python and SQL extensively to build models, data pipelines, testing tools, and analyses.
• 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

R

SAS

NLTK

Scikit-Learn

Excel

Tableau

Power BI

Jupyter

Azure

Hadoop

Education

Bachelor's degree in computer science, data science, statistics, applied mathematics or related field

Tools

Azure AI services

Job description

DLA Piper LLP (US) is seeking a Data Scientist in Applied AI Solutions to design, deploy, and scale data models and ML solutions that improve client outcomes. You will translate legal and regulatory needs into analytic objectives, develop testing frameworks, and collaborate with lawyers and clients to deliver measurable business impact.

The role emphasizes bias/accuracy testing, working with cloud platforms like Azure, and building end-to-end data pipelines using Python and SQL in a hybrid U.S.