Senior Data Scientist

A Typical Day in the Life Includes:

• Own data science workstreams end to end. Take business questions from initial scoping through data analysis, insight synthesis, and stakeholder communication. Manage day-to-day relationships with business partners. Develop exploratory insights, statistical models, and causal frameworks as appropriate.
• Frame problems before solving them. Work with stakeholders to translate business objectives into clear analytical frameworks. Influence problem framing to ensure questions can be answered using data science.
• Translate findings into decisions. Develop clear narratives and exec-facing presentations that connect analytical results to business decisions. Prioritize actionability and connection to business outcomes without sacrificing methodological transparency.
• Collaborate across team on data foundation and DS practices. Work as part of a cohesive data science pod to share best practices, innovations and business/data knowledge.
• Support turning insights into actions. Develop production-ready analytical scripts and contribute to collaborations with IT on deployment of insights into operational workflows.

Basic Qualifications:

• Machine learning and statistical modeling experience. 7+ years of experience applying machine learning, statistical modeling, predictive analytics, and data science techniques to solve complex business problems. Strong understanding of supervised learning, model evaluation, feature engineering, and statistical inference.
• Generative AI and RAG experience. 1+ years of hands-on experience working with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), prompt engineering, evaluation frameworks, and AI-powered business applications. Experience translating emerging AI capabilities into practical business solutions.
• SaaS product, sales, or go-to-market analytics background. Significant experience working on customer acquisition, pipeline generation, opportunity prioritization, account scoring, or sales analytics in a B2B SaaS or enterprise software environment. Comfort thinking in terms of funnels, conversion metrics, account engagement, and revenue outcomes.
• End-to-end analytical ownership. Demonstrated ability to independently own a full analytical project, from problem framing to data acquisition and preparation, analysis, modeling, and insight communication, all with minimal technical supervision.
• Strong SQL and Python skills. Ability to independently explore and transform data, build analytical datasets, and implement statistical and machine learning models. Comfort working in Snowflake.
• Stakeholder-facing communication. Proven ability to present analytical findings to business stakeholders in a clear, compelling, and understandable way. Experience relating methodology to business narrative and clearly identifying recommended actions for Sales, Marketing, and business leaders.
• Statistical rigor. Solid grounding in statistical methods relevant to business analytics and machine learning: regression modeling, segmentation, experimentation, propensity modeling, and predictive analytics. Ability to explain methodological choices clearly and evaluate and articulate tradeoffs between approaches.
• Legal authorization to work permanently in Mexico for any employer without requiring a visa transfer or visa sponsorship now or in the future.

Preferred Qualifications:

• Sales, Marketing, or Go-to-Market domain experience. Prior work supporting or embedded within a Sales, Marketing, Revenue Operations, or Go-to-Market organization, or direct experience with customer acquisition, pipeline generation, opportunity management, and account prioritization analytics.
• Experience influencing business decisions with data. Examples of analytical work that demonstrably changed a business decision or drove a stakeholder to action that measurably impacted business outcomes, pipeline, revenue, or other key performance indicators.
• Data visualization and storytelling. Strong demonstrated instinct for visual communication of quantitative findings and translating analytical results into clear business recommendations.
• Experience with AI and data science workflows. Comfort using generative AI tools to accelerate data science workflows. Experience applying Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), or AI-powered solutions to business problems is preferred.

Originally posted on [Employer]