Lead data science team and drive end-to-end client workstreams with scalable, business-focused insights.

• Lead, mentor and grow a team of 7–10 data scientists, fostering a high-performance and collaborative culture
• Establish best practices for modeling, code quality and reproducibility across engagements
• Drive the development of new analytical frameworks, tools and reusable assets
• Oversee end-to-end data science workstreams across multiple client engagements
• Ensure rigor, scalability and business relevance of all models and analyses
• Translate complex analytical outputs into clear, actionable insights for executive stakeholders
• Own and evolve core methodologies across areas such as customer targeting, segmentation, cross-sell, churn prevention, forecasting/quota optimization and marketing performance optimization
• Develop and apply machine learning models (e.g., unsupervised and supervised learning, time series, NLP-based approaches) to solve GTM challenges
• Explore and integrate emerging AI capabilities (e.g., LLMs, automation, augmentation tools) into client solutions and internal workflows
• Partner with consulting leaders to support business development efforts as a technical expert

Requirements

• Candidates MUST BE fully authorized to work in the U.S. without current or future need or potential need for visa sponsorship
• 5–10+ years of experience in data science, analytics or applied machine learning roles
• Proven experience leading teams and managing complex analytical projects
• Strong proficiency in Python (pandas, scikit-learn, etc.) and SQL; familiarity with R
• Experience building and deploying predictive models in business contexts
• Strong familiarity with RevOps, sales or GTM data (e.g., CRM, pipeline, account-level data)
• Ability to connect data science work to business outcomes, particularly in B2B environments
• Strong communication skills with the ability to explain technical concepts to non-technical stakeholders
• Experience in Consulting, Private Equity or enterprise B2B environments is highly preferred
• Experience working with cloud platforms and modern data infrastructure, including AWS, Azure or GCP, as well as warehouse/lakehouse platforms such as Snowflake and Databricks a plus

Preferred

• Experience in Consulting, Private Equity or enterprise B2B environments is highly preferred
• Experience working with cloud platforms and modern data infrastructure, including AWS, Azure or GCP, as well as warehouse/lakehouse platforms such as Snowflake and Databricks a plus
• Exposure to production ML systems (MLOps, model deployment, monitoring) a plus
• Experience building internal data products or client-facing analytical tools (e.g., dashboards, apps or reusable platforms) a plus
• Experience with NLP, LLMs or AI-driven applications a plus