Sr. Manager Of Data Science

TEKsystems•Raleigh, NC

1d•$85 - $96•Hybrid

About The Position

We are seeking a hands-on Senior Manager of Data Science to lead a high-impact team in developing the strategy, standards, and execution of AI across our content ecosystem. You will lead a team that embeds machine learning and generative AI directly into production systems operating at scale. This applied research role balances innovation with practical constraints (e.g. latency, cost, reliability), requiring a strong ability to quickly iterate on prototypes (e.g. “vibe coding”), communicate tradeoffs, and rapidly deploy to production environments. This role is central to our transformation toward an intelligent, agent-enabled content platform which is capable of grounded reasoning, turning structured and unstructured data sources into legal knowledge.

Requirements

Advanced degree (Master’s or PhD) in Data Science, Computer Science, Statistics, or a related field strongly preferred, or equivalent practical experience
Bachelor’s degree in a relevant field with significant applied experience in data science, machine learning, or AI
8+ years of relevant experience in data science, machine learning, or applied AI
4+ years of leadership experience (direct or indirect team management)
Proficient with Python, ML and LLM tooling such as Google ADK, LangChain, ML Frameworks (e.g. TensorFlow, PyTorch) and prompt tuning techniques.
Familiarity with vector databases, knowledge graphs, and hybrid retrieval architecture.
Strong experience working with structured and unstructured data at scale.
Ability to design and implement data pipelines and preparation workflows.
Experience integrating ML into complex, multi-stage processing systems
Working knowledge of containerization, CI/CD, RESTful API Design and model serving tools.
Cloud infrastructure experience on AWS (preferred), Azure, or GCP.
Familiarity with AI Coding Tools (e.g. GitHub CoPilot, Claude Code, OpenAI Codex)

Nice To Haves

Graduate degree in Computer Science, AI, Machine Learning, or equivalent experience.
8+ years of post-degree experience, with 4+ years in a data science or applied AI leadership role, with a focus on NLP/LLM systems.
Prior experience in legal tech, legal AI, or document-intensive domains is highly desirable.
Familiarity with ethical/legal considerations in deploying generative AI in professional settings.

Responsibilities

Set the vision and strategic priorities, acting as a recognized expert for Data Science
Lead and develop a team of data scientists and ML engineers, setting the cultural tone for the group
Drive applied research with a clear path to production, explicitly balancing innovation against real-world constraints including latency, cost, and reliability
Build and scale evaluation science capabilities within the team, including offline evaluation frameworks, automated benchmarking pipelines, and human-in-the-loop feedback systems to rigorously measure model quality and business impact
Champion hands-on rapid prototyping and iteration
Collaborate with other Data Science teams to maximize re-use of components and patterns, eliminating waste, duplication and unnecessary customization
Operate with broad scope, coordinating across multiple cross-functional teams, systems, and domains
Collaborate closely with other Data Science teams, to define and execute the AI roadmap across the content lifecycle, maximizing reuse in areas including: Content collection (e.g. “web scraping”) and transformation, Metadata extraction, enrichment, and classification, Agentic workflows turning real-world events and legal content into legal intelligence, AI-powered downstream product capabilities
Design and deploy scalable, production-grade AI systems, including: LLM-powered document understanding and generation, Agentic workflows balancing agent autonomy and efficiency with required structure and accuracy, Retrieval-augmented generation (RAG) pipelines, Hybrid ML + rules-based systems for structured content
Lead through execution and by example: Actively writing code, not just delegating, Building and demoing working prototypes (e.g. by “vibe coding”), Directly contributing to experiments and production models
Establish and scale best practices in Data Science, including: Model development, evaluation, and monitoring, Prompt engineering and experimentation frameworks, Data preparation and feature engineering standards, Reusable components and platform capabilities
Partner closely with engineering, architecture, and product leaders to: Integrate AI into large-scale distributed systems, Ensure performance, scalability, and reliability, Align technical solutions with business outcomes
Translate complex, ambiguous problems into clear project plans and executable solutions, and lead teams through delivery
Present tradeoffs, alternative approaches and options when faced with delivery constraints
Mentor and grow a multidisciplinary team of LLM-focused Data Scientists and ML Engineers.
Drive cross-functional collaboration with Legal SMEs, Data Engineers, Product Managers, and Design.
Establish best practices for evaluation, observability, and responsible use of generative AI.
Oversee development of infrastructure to support continuous delivery and monitoring of LLM systems in production environments.