Senior Data Scientist, People Analytics

Avalara•UNAVAILABLE, UNAVAILABLE

11d•Remote

About The Position

Avalara is looking for a senior People Analytics Data Scientist who will architect advanced analytics and AI-powered solutions that shape our global workforce strategy. In this role, you'll go far beyond traditional reporting and apply machine learning, causal inference, and AI-augmented listening tools to answer the most complex questions about how people work, grow, and thrive at Avalara. True end-to-end ownership is a key aspect of this role, encompassing building data foundations, designing AI-assisted listening programs, and delivering predictive insights that drive executive decisions. The position operates at the intersection of people science, AI, and strategy, requiring partnership with senior People, Finance, and business leaders within a rigorous, evidence-based global SaaS environment. This role reports to the Head of People Analytics.

Requirements

5+ years applying advanced analytics methods (experimental design, quasi-experimental approaches, predictive modeling) to people or business decisions.
Proficiency in Python and SQL; experience with R is a plus.
Hands-on experience building, validating, and iterating on statistical or ML models using large, complex datasets.
Experience with employee listening platforms (e.g., Qualtrics, Glint, Culture Amp, Medallia) and the ability to design analytics strategies around them.
Demonstrated use of NLP, text analytics, or sentiment analysis to extract insight from unstructured employee feedback data.
Familiarity with AI/ML tools and frameworks—including LLMs and generative AI—and a track record of applying them to real-world analytics problems.
Proven ability to deliver end-to-end analytical projects, from problem definition through insight adoption, in a fast-paced environment.
Experience influencing senior, non-technical stakeholders with data and translating analytical complexity into clear recommendations.
Bachelor's degree or higher in a quantitative field (statistics, economics, data science, computer science, I/O psychology) or equivalent practical experience.
Embracing AI as an essential capability.
Experience using AI and AI-related technologies.
Applying AI every day to business challenges.
Staying curious about new AI trends and best practices, and sharing knowledge.

Nice To Haves

Experience with R is a plus.

Responsibilities

Design and deploy machine learning and predictive models, including natural language processing (NLP), sentiment analysis, and generative AI applications to surface workforce signals at scale.
Leverage AI tools to automate data pipelines, accelerate hypothesis testing, and enhance the speed and accuracy of insight delivery.
Serve as a practitioner and advocate for AI-first analytics, helping the People team adopt new AI capabilities and embedding them into everyday workflows.
Lead the analytics approach for employee listening programs, including engagement surveys, pulse checks, lifecycle touchpoints, and always-on feedback channels—translating signal into actionable intelligence.
Build NLP and text analytics models to mine open-ended survey responses, exit interviews, and qualitative feedback at scale, identifying themes, sentiment trends, and early attrition signals.
Design listening architectures that integrate structured (HRIS, performance) and unstructured (survey, verbatim, Slack/Teams signals) data to produce a holistic view of the employee experience.
Partner with People leaders to close the loop and translate listening insights into targeted interventions and measuring their impact over time.
Design and execute rigorous analyses using experimental design, causal inference, and quasi-experimental methods (e.g., difference-in-differences, propensity score matching) to evaluate the effectiveness of people programs.
Build, validate, and refine predictive and explanatory models using R, Python, and SQL against large, complex HR and business datasets.
Manage the full analytical lifecycle: data preparation, feature engineering, model evaluation, and stakeholder-ready insight delivery.
Collaborate directly with senior People, Finance, and business leaders to frame hypotheses, ensure analytical rigor, and champion data-driven decision-making.
Communicate complex analytical findings—including AI model outputs and listening program results—in clear, compelling narratives for non-technical executive audiences.
Deliver multiple high-impact analytical workstreams in parallel, balancing rigor, speed, and business relevance.