Responsibilities

• Build and maintain advanced models : Build and maintain advanced predictive and time-series models: design, train, deploy, and monitor models across use cases such as demand forecasting, capacity planning, deal scoring, and customer propensity; incorporate seasonality, exogenous drivers, and backtesting frameworks to ensure accuracy and robustness
• Apply statistical rigor : lead deep-dive analyses leveraging regression, causal inference, hypothesis testing, correlation analysis, and other statistical methods to surface actionable signals from complex, large-scale datasets
• Develop AI/LLM-powered solutions : architect and implement AI-first analyses and tooling using large language models, prompt engineering, retrieval-augmented generation (RAG), and related techniques to automate insight generation, surface qualitative signals at scale, and augment team capabilities
• Own forecasting and decision systems: Own end-to-end forecasting and operational decision systems, including time-series demand forecasting, capacity planning models (e.g., Erlang-based staffing), and production pipelines that power GTM and Support planning workflows; ensure reliability, scalability, and business adoption of outputs
• Drive customer intelligence : develop and maintain prospect, deal health, archetype, and capacity models that inform GTM strategy, planning, and growth initiatives
• Define the measurement framework : identify, create, and steward benchmarks and metrics that meaningfully represent growth, engagement, and success outcomes
• Communicate with impact : distill complex analyses into clear, cohesive narratives with executive-ready materials that drive decisions at the senior leadership level
• Collaborate cross-functionally : partner with Systems & Engineering, GTM Operations, Rev Ops & Planning, Product, Business Intelligence, Data Science, and Finance to ensure analytical solutions are integrated, scalable, and trusted

Requirements

• 6+ years of professional experience in an advanced analytics or data science role; SaaS experience strongly preferred
• Deep expertise in statistical inference and modeling , including supervised techniques (regression, classification, gradient boosting, decision trees) and unsupervised techniques (clustering, PCA, anomaly detection, topic modeling)
• Hands-on experience designing and deploying AI/LLM-based solutions , including prompt engineering, fine-tuning, RAG pipelines, or LLM-integrated analytics workflows; you approach new problems with an AI-first mindset
• Familiarity and experience with distributed coding projects, including using Git for code management
• Advanced proficiency in Python (pandas, numpy, scikit-learn, xgboost, statsmodels, and LLM/AI libraries such as LangChain, OpenAI SDK, or HuggingFace) and SQL ; working knowledge of DBT
• Own and scale end-to-end data pipelines, including orchestration with Airflow and transformation/modeling with dbt; design reliable, testable, and modular workflows that support production-grade analytics and machine learning use cases, with a focus on performance, data quality, and maintainability.
• Develop and iterate on time-series forecasting frameworks using approaches such as ARIMA/SARIMAX, ETS, MSTL, and machine learning-based models; evaluate performance through rigorous backtesting and continuously improve model accuracy and business applicability
• Experience building data visualizations and dashboards across platforms such as Tableau, ThoughtSpot, matplotlib, seaborn, plotly, or similar tooling.
• Strong project ownership : experienced operating to a roadmap, managing milestones and deliverables, and delivering high-quality work product in a timely manner
• Comfortable with autonomy and ambiguity , with a proactive orientation toward identifying and solving problems before they're fully defined
• Excellent written and verbal communication skills , including experience preparing materials for executive audiences

Nice to have

• SaaS experience strongly preferred

Benefits

• comprehensive range of health, welfare, and wellbeing benefits based on eligibility.