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
Participate in client meetings, proposals and solution design as a “secondary seller”
Translate client needs into scalable analytical approaches and differentiated offerings

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
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

Benefits

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Applicant Tracking System Keywords

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Hard Skills & Tools

data scienceanalyticsapplied machine learningPythonSQLRmachine learning modelspredictive modelsNLPMLOps

Soft Skills

leadershipmentoringcommunicationcollaborationproblem-solvingbusiness acumenanalytical thinkingstakeholder managementproject managementclient engagement