Commercial Data Scientist

About Growth & Co

Growth & Co specializes in helping companies translate AI ambitions into concrete enterprise growth by creating and deploying adaptive solutions that address critical business challenges. The firm combines agentic design, operational transformation, strategic growth, and change management to deliver measurable improvements for major public and private organizations. Their team boasts a proven record of generating over $100 billion in enterprise value, facilitating clients' journey from AI ambitions to practical results and value realization.

Role Overview

We seek a highly capable data scientist who can operate independently across a variety of client scenarios, bringing a strong analytical foundation alongside sharp business intuition. The ideal individual questions effectively to define the right analyses and understands the gap between stakeholder questions and actual business needs. Initially contracted as an independent consultant, the position may transition into a full-time role based on performance and mutual fit during 2026.

Key Responsibilities

• Develop, validate, and communicate models covering segmentation and advanced analytics that answer specific commercial or operational questions, detailing underlying assumptions and limitations clearly.
• Design and interpret causal analyses including experiments and quasi-experimental methods, avoiding reliance solely on correlations where differentiation impacts decisions.
• Refine vague or ambiguous requests into precise analytical objectives before data work begins.
• Present insights effectively to senior stakeholders in ways that enable actionable decision-making rather than mere information sharing.
• Manage your workstream autonomously, including setting scope, prioritizing tasks, identifying blockers, and meeting deadlines.
• Perform light data engineering as needed to integrate and prepare data, while heavy data engineering will be handled by dedicated engineers.

Desired Qualifications and Experience

• Strong operational experience with accountability for deliverables in real business environments; consulting background is a bonus but not required.
• Exceptional problem-solving skills capable of structuring ambiguous questions, determining necessary data, and designing analytical approaches independently.
• Deep expertise in modeling spanning descriptive, diagnostic, predictive, and prescriptive analytics, including regression, classification, clustering, tree-based and ensemble methods, time-series forecasting, and propensity/uplift modeling.
• Hands-on ability to perform full modeling lifecycle: feature engineering, model selection, validation with appropriate metrics and cross-validation, avoiding overfitting and data leakage; capable of balancing model complexity judiciously.
• Fundamental data engineering capabilities to build and maintain analytic environments and pipelines for merging messy, disparate data sources.
• Proficiency across modern analytics technology stacks, comfortable with SQL for data retrieval and Python or R for analysis, utilizing libraries such as pandas, scikit-learn, and statsmodels; code is clean, reproducible, maintainable, documented, versioned, and executable by others.
• Familiarity with cloud data platforms like S3, Google Cloud Storage, BigQuery, Redshift, or similar, including ability to deploy lightweight infrastructure as needed.
• Experience with ETL and data preparation tools such as Alteryx is an advantage.
• Exposure to AI-assisted analytics approaches, including experimenting or building with large language model-based techniques, reflecting future trends in analytics.
• Preference for candidates with background in go-to-market, sales, or revenue operations analytics due to faster integration into the role.
• Strong communication skills to engage senior clients credibly and ask clarifying questions that optimize project outcomes.