Applied AI Data Scientist – Hybrid Work & Impact

DLA Piper

Maryland

Hybrid

USD 130,000 - 206,000

Full time

4 hours ago
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Benefits offered by this job

Hybrid work schedule
Medical, dental, and vision insurance
401(k) retirement plan

Job summary

DLA Piper seeks a Data Scientist, Applied AI Solutions to develop and deploy data models and machine learning solutions that deliver measurable business impact. You will translate legal and regulatory needs into analytic objectives and collaborate with lawyers, clients, and cross-functional teams to drive data-driven outcomes.

The role involves building production-grade models at scale in Azure or comparable platforms, designing testing methods, and communicating insights to diverse audiences

Qualifications

  • Bachelor's degree in a related field.
  • Minimum 5 years applying data science & ML to complex business problems.
  • Experience building and deploying production models at scale.

Responsibilities

  • Develops and applies algorithmic testing methods for AI systems, bias and accuracy.
  • Translates legal, regulatory, and business requirements into analytic objectives and data specs.
  • Builds, deploys, and monitors production ML models and data pipelines.
  • Communicates insights to both technical and non-technical audiences.

Skills

Python
SQL
Machine learning
Data science
Azure

Education

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

Tools

SAS
NLTK
Scikit-Learn
Excel
Tableau
Power BI
Jupyter
Hadoop
Azure

Job description

DLA Piper seeks a Data Scientist, Applied AI Solutions to develop and deploy data models and machine learning solutions that deliver measurable business impact. You will translate legal and regulatory needs into analytic objectives and collaborate with lawyers, clients, and cross-functional teams to drive data-driven outcomes.

The role involves building production-grade models at scale in Azure or comparable platforms, designing testing methods, and communicating insights to diverse audiences

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