Data Science Engineer Visa Sponsorship Jobs in Tennessee
Tennessee's data science engineer job market is anchored by healthcare technology in Nashville, where companies like HCA Healthcare and Vanderbilt University Medical Center drive significant demand. Memphis adds logistics and supply chain analytics roles, while Knoxville benefits from Oak Ridge National Laboratory's research pipeline. Employers across these hubs have an established history of H-1B visa sponsorship for qualified data science engineers.
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Get Access To All JobsINTRODUCTION
The Director, Data Science & AI Engineering will lead the development and execution of the Firm’s enterprise data science, analytics, and AI engineering strategy. This role is responsible for building and managing a multidisciplinary team of Data Scientists, Data Analysts, and MLOps / AI Engineers focused on delivering innovative, production-ready AI solutions that support the Firm’s legal and business operations. Key responsibilities include overseeing the design, implementation, and optimization of LLM-powered applications, retrieval-augmented generation (RAG) systems, AI agents, custom models, advanced analytics, and business intelligence platforms that provide actionable matter, financial, and operational insights to firm leadership.
This position serves as a strategic and hands-on leadership role within a rapidly evolving AI environment, responsible for establishing scalable architecture, governance, evaluation standards, security practices, and operational frameworks appropriate for a highly regulated professional services organization. The Director will work closely with the Knowledge Management & Innovation function and other firm stakeholders to translate legal and operational needs into practical AI-driven solutions that enhance efficiency, decision-making, and service delivery across the Firm.
KEY RESPONSIBILITIES
Leadership
- Design and implement the operational framework, team structure, delivery processes, and performance metrics for the firm’s Data Science & AI Engineering function.
- Recruit, develop, and lead a high-performing multidisciplinary team of Data Scientists, Data Analysts, and MLOps/AI Engineers, establishing strong technical and cultural standards.
- Oversee day-to-day team operations, including strategic planning, prioritization, project delivery, performance management, mentorship, and employee development.
- Foster a collaborative, innovative, and business-focused culture centered on delivering practical AI and analytics solutions that support legal and operational outcomes.
Build & Lead Internal AI Platforms and Solutions
- Design, develop, and deploy internal AI applications leveraging firm-approved large language models (LLMs), with a focus on scalability, evaluation, observability, and cost efficiency.
- Lead the development and management of the firm’s retrieval-augmented generation (RAG) capabilities, including ingestion pipelines, embeddings, vector and hybrid search, re-ranking, and citation-supported response generation across firm knowledge and matter data.
- Oversee integrations between AI applications and internal business systems, including document management, matter management, financial, timekeeping, and knowledge management platforms, utilizing secure and governed integration frameworks such as Model Context Protocol (MCP).
- Develop and operationalize AI-driven workflows and agent-based solutions that support legal and business processes, incorporating appropriate governance, controls, traceability, and human oversight.
- Direct model development, fine-tuning, evaluation, and optimization initiatives utilizing proprietary firm data while ensuring compliance with confidentiality, privilege, intellectual property, and security requirements.
- Lead the firm’s analytics and reporting initiatives, including data modeling, warehouse/lakehouse strategy, and the development of dashboards and business intelligence tools that provide actionable operational, financial, staffing, and AI utilization insights to firm leadership.
MLOps, Engineering Excellence, and Governance
- Establish and oversee core AI engineering and operational standards, including source control, CI/CD processes, infrastructure-as-code, observability, environment management, evaluation frameworks, and production support practices.
- Lead and maintain scalable MLOps/LLMOps capabilities, including prompt and model versioning, automated testing, performance monitoring, drift detection, latency and cost tracking, and incident management processes.
- Partner with Information Security, IT, Privacy, Risk, and the Office of General Counsel to ensure AI solutions comply with firm standards related to confidentiality, privilege, client obligations, data governance, and regulatory requirements.
- Support the development and execution of the firm’s AI governance framework, including acceptable use standards, vendor and model evaluation processes, testing protocols, and risk management practices.
- Evaluate and recommend build-versus-buy strategies for AI and technology solutions, leveraging commercial platforms where appropriate and developing custom solutions where the firm can achieve strategic or operational advantage.
Collaboration Across the Firm
- Collaborate closely with Knowledge Management & Innovation leadership to align AI development initiatives with practice group priorities, business needs, and user adoption strategies.
- Partner with attorneys, practice groups, and business stakeholders throughout the solution development lifecycle to ensure AI tools and workflows address operational and client service needs effectively.
- Communicate technical concepts, architectural decisions, and implementation trade-offs clearly and effectively to business and legal stakeholders.
- Represent the firm in interactions with vendors, clients, industry groups, peer organizations, and the broader legal AI community to support innovation, strategic partnerships, and talent development.
REQUIRED EDUCATION, KNOWLEDGE & EXPERIENCE
- Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Data Science, or a related quantitative field, or equivalent combination of education and relevant professional experience. Advanced degrees preferred.
- 10+ years of progressive experience in data science, machine learning, AI engineering, or a related technical discipline, including demonstrated leadership experience building and managing high-performing technical teams.
- Proven success designing, deploying, and supporting production AI/ML solutions, including large language model (LLM) applications, retrieval-augmented generation (RAG) systems, and agent-based workflows.
- Strong technical foundation with the ability to evaluate system architecture, engineering approaches, model performance, and technical design decisions across AI and analytics platforms.
- Deep understanding of modern AI technologies and architectures, including foundation models, embeddings, vector databases, hybrid search, RAG methodologies, agent frameworks, Model Context Protocol (MCP), prompt engineering, model evaluation, fine-tuning, and operational scalability considerations.
- Experience establishing and supporting MLOps/LLMOps practices, including cloud infrastructure, deployment automation, monitoring, security, and operational governance.
- Demonstrated experience leading enterprise analytics and business intelligence initiatives, including the development of executive-facing dashboards and reporting solutions that support strategic decision-making.
- Strong leadership, organizational, and problem-solving skills, with the ability to operate effectively in fast-paced, evolving, and highly collaborative environments.
- Excellent written and verbal communication skills, including the ability to communicate complex technical concepts, AI risks, and architectural decisions clearly to executive leadership, attorneys, and non-technical stakeholders.
- Sound judgment regarding AI governance, privacy, security, confidentiality, and ethical considerations associated with deploying AI technologies in regulated and data-sensitive environments.
PREFERRED SKILLS & QUALIFICATIONS
- Experience within the legal, professional services, financial services, or another highly regulated, document-intensive industry environment. Legal education or prior legal industry experience is a plus.
- Familiarity with legal technology and enterprise data platforms, including document management systems, matter and timekeeping systems, knowledge management platforms, eDiscovery technologies, and litigation support tools.
- Hands-on experience with AI platforms and tooling from providers such as OpenAI, Anthropic, Google, Microsoft, and open-source AI ecosystems, including familiarity with legal-specific AI platforms such as Harvey, CoCounsel, and Legora.
- Active participation in the AI engineering, machine learning, or applied research community through publications, speaking engagements, open-source contributions, or established industry networks.
Physical Requirements
- Ability to sit and stand for extended periods.
- Ability to lift up to 15 pounds.
Qualified applicants with arrest and conviction records will be considered for the position in accordance with the California Fair Chance Act.
The expected salary range for this position is $290,000 - $440,000. Final compensation will be determined based on several factors, including but not limited to, relevant experience, qualifications, skill set, and geographic location.
Pillsbury Winthrop Shaw Pittman LLP is an Equal Opportunity Employer.
If you require an accommodation in order to apply for a position, please contact us at PillsburyWorkday@pillsburylaw.com.
INTRODUCTION
The Director, Data Science & AI Engineering will lead the development and execution of the Firm’s enterprise data science, analytics, and AI engineering strategy. This role is responsible for building and managing a multidisciplinary team of Data Scientists, Data Analysts, and MLOps / AI Engineers focused on delivering innovative, production-ready AI solutions that support the Firm’s legal and business operations. Key responsibilities include overseeing the design, implementation, and optimization of LLM-powered applications, retrieval-augmented generation (RAG) systems, AI agents, custom models, advanced analytics, and business intelligence platforms that provide actionable matter, financial, and operational insights to firm leadership.
This position serves as a strategic and hands-on leadership role within a rapidly evolving AI environment, responsible for establishing scalable architecture, governance, evaluation standards, security practices, and operational frameworks appropriate for a highly regulated professional services organization. The Director will work closely with the Knowledge Management & Innovation function and other firm stakeholders to translate legal and operational needs into practical AI-driven solutions that enhance efficiency, decision-making, and service delivery across the Firm.
KEY RESPONSIBILITIES
Leadership
- Design and implement the operational framework, team structure, delivery processes, and performance metrics for the firm’s Data Science & AI Engineering function.
- Recruit, develop, and lead a high-performing multidisciplinary team of Data Scientists, Data Analysts, and MLOps/AI Engineers, establishing strong technical and cultural standards.
- Oversee day-to-day team operations, including strategic planning, prioritization, project delivery, performance management, mentorship, and employee development.
- Foster a collaborative, innovative, and business-focused culture centered on delivering practical AI and analytics solutions that support legal and operational outcomes.
Build & Lead Internal AI Platforms and Solutions
- Design, develop, and deploy internal AI applications leveraging firm-approved large language models (LLMs), with a focus on scalability, evaluation, observability, and cost efficiency.
- Lead the development and management of the firm’s retrieval-augmented generation (RAG) capabilities, including ingestion pipelines, embeddings, vector and hybrid search, re-ranking, and citation-supported response generation across firm knowledge and matter data.
- Oversee integrations between AI applications and internal business systems, including document management, matter management, financial, timekeeping, and knowledge management platforms, utilizing secure and governed integration frameworks such as Model Context Protocol (MCP).
- Develop and operationalize AI-driven workflows and agent-based solutions that support legal and business processes, incorporating appropriate governance, controls, traceability, and human oversight.
- Direct model development, fine-tuning, evaluation, and optimization initiatives utilizing proprietary firm data while ensuring compliance with confidentiality, privilege, intellectual property, and security requirements.
- Lead the firm’s analytics and reporting initiatives, including data modeling, warehouse/lakehouse strategy, and the development of dashboards and business intelligence tools that provide actionable operational, financial, staffing, and AI utilization insights to firm leadership.
MLOps, Engineering Excellence, and Governance
- Establish and oversee core AI engineering and operational standards, including source control, CI/CD processes, infrastructure-as-code, observability, environment management, evaluation frameworks, and production support practices.
- Lead and maintain scalable MLOps/LLMOps capabilities, including prompt and model versioning, automated testing, performance monitoring, drift detection, latency and cost tracking, and incident management processes.
- Partner with Information Security, IT, Privacy, Risk, and the Office of General Counsel to ensure AI solutions comply with firm standards related to confidentiality, privilege, client obligations, data governance, and regulatory requirements.
- Support the development and execution of the firm’s AI governance framework, including acceptable use standards, vendor and model evaluation processes, testing protocols, and risk management practices.
- Evaluate and recommend build-versus-buy strategies for AI and technology solutions, leveraging commercial platforms where appropriate and developing custom solutions where the firm can achieve strategic or operational advantage.
Collaboration Across the Firm
- Collaborate closely with Knowledge Management & Innovation leadership to align AI development initiatives with practice group priorities, business needs, and user adoption strategies.
- Partner with attorneys, practice groups, and business stakeholders throughout the solution development lifecycle to ensure AI tools and workflows address operational and client service needs effectively.
- Communicate technical concepts, architectural decisions, and implementation trade-offs clearly and effectively to business and legal stakeholders.
- Represent the firm in interactions with vendors, clients, industry groups, peer organizations, and the broader legal AI community to support innovation, strategic partnerships, and talent development.
REQUIRED EDUCATION, KNOWLEDGE & EXPERIENCE
- Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Data Science, or a related quantitative field, or equivalent combination of education and relevant professional experience. Advanced degrees preferred.
- 10+ years of progressive experience in data science, machine learning, AI engineering, or a related technical discipline, including demonstrated leadership experience building and managing high-performing technical teams.
- Proven success designing, deploying, and supporting production AI/ML solutions, including large language model (LLM) applications, retrieval-augmented generation (RAG) systems, and agent-based workflows.
- Strong technical foundation with the ability to evaluate system architecture, engineering approaches, model performance, and technical design decisions across AI and analytics platforms.
- Deep understanding of modern AI technologies and architectures, including foundation models, embeddings, vector databases, hybrid search, RAG methodologies, agent frameworks, Model Context Protocol (MCP), prompt engineering, model evaluation, fine-tuning, and operational scalability considerations.
- Experience establishing and supporting MLOps/LLMOps practices, including cloud infrastructure, deployment automation, monitoring, security, and operational governance.
- Demonstrated experience leading enterprise analytics and business intelligence initiatives, including the development of executive-facing dashboards and reporting solutions that support strategic decision-making.
- Strong leadership, organizational, and problem-solving skills, with the ability to operate effectively in fast-paced, evolving, and highly collaborative environments.
- Excellent written and verbal communication skills, including the ability to communicate complex technical concepts, AI risks, and architectural decisions clearly to executive leadership, attorneys, and non-technical stakeholders.
- Sound judgment regarding AI governance, privacy, security, confidentiality, and ethical considerations associated with deploying AI technologies in regulated and data-sensitive environments.
PREFERRED SKILLS & QUALIFICATIONS
- Experience within the legal, professional services, financial services, or another highly regulated, document-intensive industry environment. Legal education or prior legal industry experience is a plus.
- Familiarity with legal technology and enterprise data platforms, including document management systems, matter and timekeeping systems, knowledge management platforms, eDiscovery technologies, and litigation support tools.
- Hands-on experience with AI platforms and tooling from providers such as OpenAI, Anthropic, Google, Microsoft, and open-source AI ecosystems, including familiarity with legal-specific AI platforms such as Harvey, CoCounsel, and Legora.
- Active participation in the AI engineering, machine learning, or applied research community through publications, speaking engagements, open-source contributions, or established industry networks.
Physical Requirements
- Ability to sit and stand for extended periods.
- Ability to lift up to 15 pounds.
Qualified applicants with arrest and conviction records will be considered for the position in accordance with the California Fair Chance Act.
The expected salary range for this position is $290,000 - $440,000. Final compensation will be determined based on several factors, including but not limited to, relevant experience, qualifications, skill set, and geographic location.
Pillsbury Winthrop Shaw Pittman LLP is an Equal Opportunity Employer.
If you require an accommodation in order to apply for a position, please contact us at PillsburyWorkday@pillsburylaw.com.
Data Science Engineer Job Roles in Tennessee
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Search Data Science Engineer Jobs in TennesseeData Science Engineer Jobs in Tennessee: Frequently Asked Questions
Which companies in Tennessee sponsor visas for data science engineers?
Nashville-based healthcare giants HCA Healthcare and Change Healthcare have a consistent record of H-1B filings for data science roles. Vanderbilt University and Oak Ridge National Laboratory sponsor engineers through both H-1B and J-1 visa pathways. FedEx in Memphis sponsors data science talent for logistics analytics. Tennessee-headquartered insurers like Cigna's Nashville operations also appear regularly in Department of Labor disclosure data for these positions.
Which visa types are most common for data science engineer roles in Tennessee?
The H-1B is the dominant visa category for data science engineers in Tennessee, as the role typically qualifies as a specialty occupation requiring at least a bachelor's degree in computer science, statistics, or a related field. OPT and STEM OPT extensions are common entry points for graduates from Vanderbilt, University of Tennessee, and Belmont University before an employer files an H-1B petition on their behalf.
Which cities in Tennessee have the most data science engineer sponsorship jobs?
Nashville leads by a wide margin, driven by its concentration of healthcare IT, insurance technology, and fintech employers. Knoxville comes second, with Oak Ridge National Laboratory and University of Tennessee research affiliates generating engineering demand. Memphis contributes roles in supply chain analytics, primarily through FedEx and logistics technology firms. Chattanooga has a smaller but growing presence tied to its manufacturing and energy analytics sectors.
How to find data science engineer visa sponsorship jobs in Tennessee?
Migrate Mate is built specifically for international candidates seeking visa sponsorship roles in the United States. You can filter directly for data science engineer positions in Tennessee, with results drawn from employers that have a documented history of sponsoring work visas. This saves significant time compared to manually cross-referencing job postings with Department of Labor LCA disclosure data to verify sponsorship history.
Are there any state-specific considerations for data science engineers pursuing sponsorship in Tennessee?
Tennessee has no state income tax on wages, which affects the prevailing wage calculations employers use when filing Labor Condition Applications, as the DOL sets wage floors by metropolitan area rather than state tax treatment. The Oak Ridge and Nashville corridors also attract candidates with security clearance eligibility, since some federal contractor roles there require U.S. citizenship or permanent residency, effectively limiting sponsorship to the commercial sector for most international candidates.
What is the prevailing wage for sponsored data science engineer jobs in Tennessee?
U.S. employers sponsoring a visa must pay at least the prevailing wage, which is what workers in the same role, area, and experience level typically earn. The Department of Labor sets this rate to make sure companies aren't hiring foreign workers simply because they'd accept lower pay than a U.S. worker. It varies by job title, location, and experience. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search page.