Data Science Practice Lead

Job Description:

Data Science Practice Lead

We are seeking an experienced Data Science Practice Lead to help drive and scale data science use cases and AI-enabled capabilities for internal technology and operations. In this Boston-based leadership role, you will focus on internal-facing use cases, supporting teams like customer support/call centers, document workflow, compliance, finance, and other corporate functions. The ideal candidate combines deep technical data science expertise with strong leadership and business collaboration skills. You will guide a team of data scientists to deliver innovative solutions, working closely with business stakeholders and engineering partners to ensure projects are well-scoped, aligned to business needs and enterprise standards. A key aspect of this role is driving experimentation and pilot projects to prove value before scaling solutions to full production. If you are passionate about making measurable operational improvements and can communicate clearly to both technical and non-technical audiences, we want to hear from you.

Key Responsibilities:

• Identify and Scope High-Impact Use Cases: Work directly with internal business stakeholders to identify high-value internal problems and frame them into AI use cases. Translate business needs into AI/ML capabilities, experiments, and measurable outcomes that align solution delivery with business objectives.
• Drive Experimentation and Pilot Solutions: Lead prototyping efforts and experimental pilot programs to validate solution approaches and quantify business value before full-scale deployment. Employ an iterative, test-and-learn approach - designing A/B tests or proof-of-concepts to quickly learn what works and adjusting based on data-driven learnings and business stakeholder input. Ensure success metrics and KPIs are defined for each initiative to objectively evaluate impact.
• Leadership and Team Development: Guide and mentor a team of data scientists, providing technical direction and oversight. Set the example for best practices in modeling, coding, and project execution. Manage project portfolios to ensure multiple workstreams progress on schedule and deliver high-quality results, while fostering a culture of curiosity, collaboration, and continuous learning on the team.
• Cross-Functional Collaboration and Delivery: Partner closely with engineering and architecture teams to implement data science solutions into production. Oversee the development of robust data pipelines and integration of models into existing systems, ensuring solutions are scalable, well-documented, and maintainable. Work with business stakeholders (e.g. operations, finance, HR, compliance) to pilot and roll out tools that create efficiencies and drive value, making sure these solutions fit seamlessly into business operations.
• Communication and Stakeholder Management: Communicate progress, tradeoffs, and recommendations in clear, impactful ways. Translate technical concepts into business terms, including end-to-end impact of AI solutions, total cost (build/maintain), risks and controls, and value realized against agreed KPIs. Present solution performance and insights to non-technical leaders using compelling storytelling and visualizations. Regularly update stakeholders, align on success metrics, and drive adoption and change management so solutions are used and sustained.

• Education and Experience: Bachelor's degree in Statistics, Computer Science, Data Analytics, or a related quantitative field. 15 years of hands-on experience in data science or analytics (with at least a few years in a senior or team lead capacity) delivering business-focused solutions. Proven track record of end-to-end project ownership, from initial concept and prototyping to deploying models into production and iterating on them post-launch.
• Business Acumen and Stakeholder Collaboration: Demonstrated ability to work closely with business stakeholders to understand operational processes and challenges and translate them into data analysis or machine learning solutions. Strong project management skills, with an ability to prioritize projects by business need and value impact. Able to serve as a trusted advisor to cross-functional leaders by providing actionable insights that inform strategy and decision-making.
• Communication Skills: Excellent written and verbal communication skills, including the ability to distill complex analytical findings into clear presentations for non-technical audiences. Proven experience communicating data stories and recommendations to influence senior executives and frontline operational teams alike.
• Analytical Mindset: Strong problem-solving orientation with a data-driven and experimental mindset. Comfortable designing hypotheses, setting up experiments or analyses to test them, and making pragmatic decisions based on results. Able to ask the right questions and pursue whatever data or analyses are needed to answer them. Highly detail-oriented with a commitment to data quality, validation, and rigorous methodology.

• Advanced Degree: Master's or PhD in a relevant field (e.g. Data Science, Statistics, Computer Science, Engineering, etc.) for deeper theoretical foundation.
• Industry/Domain Experience: Experience applying data science in internal/corporate operations contexts - for instance, analytics projects in call centers, back-office processes, compliance, or other support functions. Familiarity with operational metrics and challenges in these domains can help you hit the ground running.
• Experimentation and Agile Methods: Hands-on experience designing and analyzing experiments (A/B tests or pilots) to evaluate solution impact is a strong plus. Knowledge of Agile project management or iterative development methodologies to drive analytics projects from conception through completion is desirable.
• Advanced Analytics and Tools: Demonstrated experience with modern NLP and Generative AI techniques (LLMs, RAG, agentic workflows, and multimodal where relevant). Hands-on experience with LLM evaluation and observability/tracing practices (e.g., experiment tracking, prompt/model evaluation, runtime monitoring, and debugging of agent behavior). Experience implementing safety and compliance guardrails and governance controls for enterprise GenAI deployments. Familiarity with data visualization and BI tools (Tableau, Power BI, etc.) for dashboarding and reporting to stakeholders is a plus.