Data Science Manager

Job Description

Scope & Impact

• Set the vision and strategic priorities for AI across the content platform, acting as a recognized expert for Data Science

• Own delivery of your assigned content streams — quality, timeliness, and automation level — while contributing reusable capability back to the shared platform

• Lead and develop a team of data scientists, setting the cultural tone for the group

• Drive applied research with a clear path to production, keeping the business outcome as the first priority and working within real-world constraints such as latency 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

• Exercise judgment about where to automate, where to keep a human editor in the loop, and how to move that line over time

• Select the right tools and technologies for the business problem

Technical & Product Leadership

• Define and execute the AI roadmap for the content platform, prioritizing reusable platform capabilities and agent-based workflows over one-off solutions.

• Translate ambiguous business problems into clear technical strategies and delivery plans, identifying tradeoffs and alternative approaches when constraints arise.

• Design and oversee production-grade AI systems that meet customer requirements for accuracy, reliability, scalability, and appropriate human oversight.

• Partner with Product, Engineering, and Architecture leaders to establish shared foundations, integrate AI into the platform at scale, and replace bespoke tooling with reusable workflows.

• Lead by example through hands-on technical contributions, including writing code, developing and demonstrating prototypes, and contributing to experiments and production models.

• Establish and scale Data Science standards for experimentation, evaluation, deployment, monitoring, performance, and reliability across both the team’s solutions and shared capabilities.

Team & Operational Excellence

• Foster a culture of curiosity, adaptability, responsible innovation, knowledge sharing, and continuous learning, enabling the team to evolve as technologies, customer needs, and business priorities change.

• Build, mentor, and develop a high-performing data science team, supporting individual growth and career development.

• Establish clear goals, priorities, operating rhythms, and accountability for the team’s work.

• Foster effective collaboration across Product, Engineering, Design, Legal, and other business functions.

• Promote a culture of technical excellence, responsible innovation, knowledge sharing, and continuous improvement.

• Ensure the team has the skills, resources, and organizational support needed to deliver against business priorities.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to View email address on click.appcast.io learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy:

Skills and Requirements

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:

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

o 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.

Technical Proficiency

• Proficient with Python, ML and LLM tooling such as Google ADK, LangChain/LangGraph, ML frameworks (e.g. TensorFlow, PyTorch) and prompt tuning techniques

• Experience building multi-agent or orchestrated LLM systems — task decomposition, tool use, routing, state and failure handling

• 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, including event-driven architectures

• Working knowledge of containerization, CI/CD, RESTful API design and model serving tools

• Familiarity with LLM observability and evaluation tooling (tracing, offline eval harnesses, LLM-as-judge and human review pipelines)

• Cloud infrastructure experience on AWS (preferred), Azure, or GCP

• Familiarity with AI coding tools (e.g. GitHub Copilot, Claude Code, OpenAI Codex)