Sr. Manager Of Data Science

Top Skills' Details

Data Science, Machine Learning, Applied AI, Leadership

Description

Role Overview :

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 s 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.

Key Responsibilities:

Scope & Impact

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

Technical & Product Leadership:

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

Team & Operational Excellence:

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.

Core

Qualifications:

Experience & Education

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

Typically requires:

8+ years of relevant experience in data science, machine learning, or applied AI

4+ years of leadership experience (direct or indirect team management)

We recognize that exceptional candidates may follow non-traditional paths and value demonstrated impact, technical depth, and leadership over strict credential requirements.

Success in this role requires:

Leading through both technical expertise and organizational influence

Acting as a change agent, embedding best practices into workflows and systems

Driving both team development and strategic outcomes across a broad scope

Ability to select the right tools and technologies to solve business problems

Technical Proficiency

Proficient with Python, ML and LLM tooling such as Google ADK, Lang Chain, ML Frameworks (e.g. Tensor Flow, PyTorch) and prompt tuning techniques.

Familiarity with vector databases, knowledge…