Lead Data Scientist

Position Overview

The Data Science team at Grant Thornton Stax delivers rigorous, data-driven quantitative analyses that inform strategic decision-making for our clients. We are seeking highly driven professionals with a proven track record of solving complex business problems using advanced data science techniques.

As a Lead Data Scientist, you will own and lead Data Science workstreams across engagements, partnering closely with generalist and technical consultants to source, transform, and analyze data to generate actionable insights. You will play a critical role in translating ambiguous business questions into structured analytical approaches and delivering client-ready outputs.

In addition, you will contribute to building scalable tools and AI-enabled solutions that enhance the firm’s value proposition and improve operational efficiency.

Responsibilities

• Lead end-to-end data science workstreams—from data sourcing and processing to analysis and insight generation
• Translate ambiguous business problems into structured analytical approaches and executable workplans
• Engage stakeholders to understand key objectives and deliver clear, actionable, client-ready insights
• Analyze large, complex datasets (internal and external) using Python and related tools
• Extract and build datasets using techniques such as APIs, web scraping, and other structured/unstructured methods
• Build, deploy, and optimize data science and AI-driven solutions (including Azure AI and related technologies)
• Continuously improve existing tools and workflows while evaluating emerging technologies for business impact
• Communicate complex technical concepts clearly to both technical and non-technical audiences

Required Skills & Expertise

• 5+ years of experience in data science or analytics, with ownership of end-to-end analytical workstreams
• Experience in consulting or working with business stakeholders, with exposure to global clients, cross-border teams, or international study/professional environments
• Proven ability to solve complex, ambiguous problems independently, with a structured approach
• Strong programming proficiency in Python and/or R
• Hands-on experience applying statistical and machine learning techniques (e.g., regression, clustering, classification, NLP)
• Experience working with large, diverse datasets to build analytical or predictive solutions
• Strong understanding of business metrics and frameworks (e.g., LTV/CAC, retention, cohort analysis, TAM)
• Ability to communicate insights clearly and translate technical outputs into business implications