Data Scientist, Applied AI Solutions

DLA Piper•Short Hills, NJ

6d•$129,808 - $206,399•Hybrid

About The Position

The Data Scientist, Applied AI Solutions collects, explores, and analyzes large, complex datasets to improve business and client outcomes. The role designs and deploys data models, algorithms, and machine learning solutions; performs custom analyses; and translates business, legal, and regulatory requirements into practical analytic approaches. The position also develops and applies algorithmic testing methods to evaluate AI systems and tools, including bias and accuracy. Working with lawyers, clients, and cross-functional teams, the Data Scientist, Applied AI Solutions delivers data-driven solutions with measurable business impact.

Requirements

Minimum 5 years of relevant experience applying data science and machine learning techniques to complex business problems, including building and deploying production models at scale.
Significant hands-on coding experience in Python and SQL is required, including building, deploying, and supporting production machine learning models at scale.
Experience managing structured and unstructured datasets in enterprise environments and working with Azure AI services or comparable enterprise cloud and data platforms is required.
Must be able to design and execute algorithmic testing of AI systems, including bias and accuracy assessments, and create novel evaluation methods when established approaches are insufficient.
Must be able to translate legal and regulatory requirements into analytic methods, testable criteria, and defensible technical documentation.
Must be self-driven, curious and creative.
Experience must include creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, data mining techniques, etc.
Experience with most or all of the following basic data science tools: Python, R, SQL, SAS, NLTK, Scikit-Learn, Excel, Tableau, Power BI, and Jupyter; Basic data science concepts: probability, statistics, hypothesis testing, machine learning, natural language processing, predictive modeling, data visualization; and Big data tools: Hadoop, Azure, etc.
Must be adept in agile methodologies and well-versed in machine learning, artificial intelligence and constructing data science pipelines.
Demonstrated presentation skills along with strong business acumen.
Ability to communicate actionable insights using data, often for a non-technical audience.
Communicate effectively, both verbally and in writing, with clients, lawyers, business professionals, and external audiences.
Produce high-quality work and respond to correspondence in an efficient, timely, and professional manner.
Meet deadlines, manage competing priorities, and commit to meeting high standards.
Comply with firm policies and procedures.
Maintain confidences as required in a law firm environment.

Nice To Haves

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.
Preferred experience includes delivering data science solutions within legal, eDiscovery, investigations, privilege review, or regulatory compliance environments.

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.
Other duties as assigned.