Principal Data Scientist - Advanced Analytics

Job description

**This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Principal Data Scientist – Advanced Analytics based in the United States.** In this senior individual contributor role, you will shape advanced analytics, experimentation, and measurement strategies that drive meaningful business outcomes. You will combine statistical rigor, causal inference, machine learning, and Generative AI to solve complex challenges and uncover new growth opportunities. Working closely with executive leaders and cross-functional teams, you will design experimentation frameworks, develop scalable analytical solutions, and turn large, complex datasets into actionable recommendations. You will also establish technical standards, strengthen data-driven decision-making practices, and help advance the adoption of emerging AI technologies. This is an opportunity to influence organizational strategy, mentor fellow data scientists, and make a measurable impact in a collaborative, innovation-focused environment. ### Accountabilities - Lead the end-to-end design, execution, and analysis of marketing and product experiments, including A/B testing, multivariate testing, geographic experiments, and holdout testing. - Define and manage the experimentation roadmap, including hypothesis development, prioritization, statistical power analysis, and success metrics. - Develop and refine methodologies for lift measurement, incrementality testing, causal inference, and marketing effectiveness measurement. - Establish experimentation standards, governance frameworks, and best practices to ensure statistical rigor and consistent, actionable insights. - Design, build, validate, and deploy machine learning and Generative AI solutions using large-scale structured and unstructured datasets. - Develop predictive and prescriptive models, including market mix, attribution, and uplift models, to improve business performance and measurable outcomes. - Lead hands-on model development, coding, testing, validation, and production deployment while establishing technical standards for the broader analytics team. - Build and maintain reliable, scalable data pipelines that support data quality, integrity, and analytical performance. - Evaluate emerging technologies, machine learning techniques, large language models, and agent-based frameworks to identify opportunities for innovation. - Partner with Product, Marketing, Sales, IT, Risk, and Compliance teams to identify high-value business challenges and translate them into testable hypotheses and analytical solutions. - Communicate complex findings and recommendations clearly to technical teams, business stakeholders, and executive leadership. - Act as a trusted subject matter expert in experimentation, measurement, advanced analytics, AI, and machine learning. - Mentor and support other data scientists and analytics professionals through coaching, technical guidance, and knowledge sharing. - Champion a data-driven culture focused on experimentation, continuous improvement, innovation, and evidence-based decision-making. ### Requirements: - Master’s degree in Statistics, Data Science, Computer Science, Economics, Mathematics, Engineering, or a related quantitative discipline is required. - PhD in Statistics, Data Science, or a related quantitative field is strongly preferred. - At least eight years of progressive experience in data science, analytics, or machine learning, including substantial hands-on work with large-scale data environments. - Demonstrated success leading or contributing as a senior technical expert to experimentation, measurement, and advanced analytics initiatives. - Deep expertise in marketing experimentation, including experimental design, A/B and multivariate testing, metric development, lift measurement, and incrementality analysis. - Strong knowledge of causal inference, hypothesis testing, statistical modeling, and statistical power analysis. - Hands-on experience developing and deploying market mix models (MMM), attribution models, and uplift models. - Advanced programming skills in Python, Spark/PySpark, and SQL. - Practical experience developing and deploying machine learning models using techniques such as clustering, classification, regression, neural networks, anomaly detection, recommendation engines, and predictive modeling. - Experience with large language models (LLMs), Generative AI solutions, and emerging agent-based frameworks such as LangChain, LangSmith, or comparable technologies. - Familiarity with modern cloud-based data platforms, including Databricks, Snowflake, or similar data lake environments. - Experience building and deploying analytical solutions in AWS and Azure cloud environments. - Familiarity with containerization and development tools such as Kubernetes, Docker, and Git. - Experience with data visualization and business intelligence tools; Tableau is preferred. Familiarity with data science platforms such as Dataiku is an advantage. - Experience working in financial services or another highly regulated, data-intensive industry is preferred. - Proven ability to influence senior stakeholders, collaborate across business functions, and translate complex challenges into scalable analytical solutions with measurable impact. - Strong leadership, mentoring, communication, and storytelling skills, including the ability to present technical findings as clear, executive-level recommendations. - Ability to navigate ambiguity, prioritize competing demands, manage multiple initiatives, and deliver results in a fast-paced environment. - Relevant certifications in data science, machine learning, or cloud technologies are a plus. ### Benefits: - **Annual salary range:** $170,000–$200,000. - Eligibility for an annual bonus incentive program. - Workplace flexibility and a collaborative working environment. - Opportunities to shape advanced analytics strategy and influence high-impact business decisions. - Professional growth through exposure to emerging AI technologies, experimentation methodologies, and large-scale data science initiatives. - Opportunities to mentor colleagues, establish best practices, and contribute to a culture of innovation and continuous learning. - Commitment to diversity, inclusion, and equal employment opportunity. - Reasonable accommodations available for applicants with disabilities during the hiring process.