Note: The job is a remote job and is open to candidates in USA. Growth & Co helps organizations turn AI ambition into tangible enterprise growth by designing and deploying agentic solutions that address high-value business problems. They are looking for a strong data scientist to work across diverse client contexts, focusing on commercial transformation and delivering actionable insights through advanced analytics.
Responsibilities
- Building and validating models, segmentation, and advanced analytics — from diagnostic and predictive through to prescriptive — that answer specific commercial or operational questions, and communicating the logic, assumptions, and limitations clearly
- Designing and interpreting analyses where causality matters (experiments, quasi-experimental methods) rather than defaulting to correlation when a decision hinges on the difference
- Translating ambiguous or loosely defined stakeholder requests into a crisp analytical brief before touching data
- Delivering findings to senior client stakeholders in formats that drive decisions, not just inform them
- Managing your own workstream with minimal oversight — scoping, prioritizing, flagging blockers, and delivering on time
- The work may include some light data engineering to combine and transform data when needed, but there will be data engineers doing the heavy lifting of this work
Skills
- Strong first-principles problem-solving. You can structure an ambiguous question, identify what data would actually answer it, and build a path there — without being handed a template
- Modeling depth with judgment. Fluent across descriptive, diagnostic, predictive, and prescriptive work, and hands-on with the core method families behind commercial problems — regression and classification, clustering and segmentation, tree-based and ensemble methods (e.g., gradient boosting), time-series forecasting, and propensity/uplift modeling. You own the full modeling arc: feature engineering, model selection, and honest evaluation — appropriate metrics, cross-validation or holdout testing, and guarding against overfitting and data leakage. Just as important, you know when a simple model beats a sophisticated one and can tell the right tool from overkill. Rigor in validation is assumed, not optional
- Data engineering fundamentals. While not core to the role, there may be situations when you need to set up an analytics environment, build a quick pipeline and ELT approach for messy data from disparate sources, write transformations, catch anomalies, and produce a data model that gives you the operational ability to build models
- Fluency across the modern analytics stack. You move comfortably between SQL for data access and Python or R for analysis and modeling, with the surrounding ecosystem (pandas, scikit-learn, statsmodels, and the like) as second nature. You write clean, reproducible, maintainable work — versioned, documented, and re-runnable by someone other than you, not one-off scripts that only work on your machine — and you work fluently alongside AI coding assistants
- Familiarity with cloud data platforms and the ability to stand up lightweight infrastructure independently when needed (S3, GCS, BigQuery, Redshift, or equivalent)
- Working knowledge of AI-assisted analytics — you've built or experimented with LLM-based analytics. This is an emerging area, but also represents the future, so it's important that you are intellectually and operationally driving toward it
- Communication skills suited to senior client environments. Not polished-for-polish's-sake, but able to earn credibility with stakeholders, and ask clarifying questions that sometimes save days of wrong work
- Consulting experience is a plus
- Experience with tools like Alteryx or similar ETL/data-prep platforms is a meaningful advantage
- Prior experience with GTM, sales, or revenue operations analytics is a strong preference but not a prerequisite; candidates with this background will ramp materially faster
Company Overview