Staff Data Scientist - Experimentation & Causal Inference in United States Embassy at Jobgether

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Data Scientist - Experimentation & Causal Inference based in United States.

This role offers the opportunity to define how a rapidly scaling technology organization learns from data and makes high-impact product decisions.
The position focuses on building a world-class experimentation and causal inference practice that enables smarter, evidence-based strategies.
You will establish methodologies, frameworks, and standards that improve how teams design, analyze, and act on experiments.
Working across product, analytics, engineering, and leadership teams, you will translate complex statistical insights into clear business recommendations.
This is a hands-on individual contributor role with significant influence, executive visibility, and the opportunity to shape a growing data science function.
The ideal candidate thrives in fast-paced environments where scientific rigor, innovation, and measurable impact drive decision-making.

The Staff Data Scientist will lead the development and adoption of experimentation and causal inference capabilities across the organization. This role requires deep statistical expertise, strong business judgment, and the ability to influence teams by creating scalable frameworks for data-driven decision-making.

• Define and establish company-wide experimentation methodologies covering hypothesis development, metrics selection, experiment design, statistical analysis, reporting, and decision-making.
• Create standards and best practices for controlled experiments to improve product development and business strategy.
• Own statistical approaches for experimentation, including significance testing, multiple comparisons, sequential testing, variance reduction techniques, and power analysis.
• Develop frameworks for analyzing complex hierarchical and clustered data structures where users, accounts, locations, and organizations interact.
• Apply advanced causal inference techniques such as matching methods, difference-in-differences, instrumental variables, and synthetic controls when controlled experiments are not possible.
• Design strategies for managing multiple concurrent experiments, including test layering, holdouts, guardrails, and contamination prevention.
• Partner with AI and machine learning teams to design evaluation frameworks for AI-powered features and fast-evolving systems.
• Lead experiment review processes and ensure conclusions are statistically sound and actionable.
• Build educational resources, templates, and training programs to improve experimentation skills across product and analytics teams.
• Collaborate with analytics engineering teams to improve data quality, governance, metric consistency, and experiment readiness.
• Influence senior stakeholders by translating complex statistical findings into clear recommendations and strategic decisions.

The ideal candidate has extensive experience in data science, applied statistics, and product experimentation, with the ability to operate as a strategic technical leader in a fast-moving technology environment. Strong communication skills and the ability to elevate teams through influence are essential.

• 9+ years of experience in data science, product analytics, applied statistics, or related fields.
• Deep hands-on experience designing, running, and analyzing online controlled experiments at scale.
• Strong foundation in applied statistics, including frequentist methods, Bayesian approaches, power analysis, variance reduction, and common experimentation challenges.
• Practical expertise in causal inference and the ability to distinguish true causal impact from bias, seasonality, selection effects, and other data limitations.
• Experience working in small-sample, high-velocity, multi-product environments where experimentation requires thoughtful judgment.
• Strong SQL skills and professional experience with Python or R.
• Ability to influence product managers, analysts, engineers, and executives without direct authority.
• Strong communication skills with the ability to explain technical concepts clearly to both technical and non-technical audiences.
• Experience building experimentation frameworks, processes, or cultures from the ground up is preferred.
• Familiarity with experimentation platforms such as Statsig is a plus.
• Background in B2B SaaS, CRM, product-led growth, multi-tenant platforms, or marketplace environments is preferred.

• Competitive compensation package aligned with experience and expertise.
• Opportunity to shape a foundational data science capability within a high-growth technology environment.
• Remote-first culture with the flexibility to work from locations that support productivity and collaboration.
• High-impact role with executive visibility and ownership over strategic initiatives.
• Opportunity to work with large-scale data systems, AI technologies, and complex product ecosystems.
• Collaborative environment that values innovation, autonomy, experimentation, and continuous learning.
• Career growth opportunities, including the potential to help build and scale a future data science team.
• Inclusive workplace culture focused on empowering employees and celebrating diverse perspectives.