Senior Business Data Scientist – Impact Measurement, Causal Inference & Analytics Strategy

About careerzynith

careerzynith is a forward-thinking, data-driven organization that partners with leading digital platforms, media brands, and technology innovators around the world. Our work sits at the intersection of business strategy, advanced analytics, and human-centered decision-making. We help some of the most influential content and creator platforms on the planet understand what truly drives impact—whether that means growing audience reach, improving creator success, optimizing monetization strategies, or building healthier digital communities.

At careerzynith, data is more than numbers on a dashboard. Data is a language we use to translate business questions into measurable insight, and insight into action. Our team operates with the rigor of a research organization and the pace of a product company. We believe that rigorous measurement, sound causal reasoning, and clear communication can fundamentally change the way organizations make decisions at scale.

Our culture is built on intellectual curiosity, collaborative problem-solving, and a shared commitment to excellence. We invest heavily in our people, providing the resources, mentorship, and flexibility needed to do the best work of their careers. If you are passionate about turning ambiguity into clarity, designing research that matters, and influencing the strategic direction of a global business, careerzynith is the place to do it.

The Opportunity

We are hiring a Senior Business Data Scientist to join our Impact Measurement team. This is a senior individual contributor role focused on causal inference, applied research, and strategic analytics for one of the largest creator and video ecosystems in the world. You will work closely with executives, business leaders, and cross-functional partners to shape the questions we ask, the methods we use, and the answers we deliver.

This role is ideal for someone who enjoys a blend of hands-on analytical work, research design, peer collaboration, and high-stakes storytelling. You will spend your time designing observational and experimental studies, writing code to analyze massive datasets, reviewing causal work from other teams, and presenting clear, honest, and actionable insights to senior stakeholders.

Key Responsibilities

• Lead Causal Research: Design and lead rigorous causal studies—both experimental and observational—to answer high-impact business questions. Translate vague strategic questions into well-defined measurement problems.
• Drive Analytical Strategy: Partner with business, product, and operations leaders to define metrics, measurement frameworks, and analytical roadmaps that shape the company’s most important decisions.
• Present to Executives: Communicate findings, recommendations, and uncertainty clearly to senior leaders, non-technical audiences, and cross-functional partners. Make complex analyses accessible and actionable.
• Peer Review and Quality Control: Serve as a peer reviewer for causal research designed by other analytics and data science teams, helping to raise the methodological bar across the organization.
• Develop and Apply New Methods: Stay current with advances in causal inference, econometrics, and applied statistics. Develop new techniques when existing methods are not sufficient.
• Build Reusable Tooling: Contribute to internal libraries, frameworks, and documentation that make high-quality causal analysis faster and more reproducible for the broader team.
• Mentor and Collaborate: Coach junior analysts and data scientists on research design, statistical thinking, and effective communication. Collaborate across time zones and disciplines.

Minimum Qualifications

• Master’s degree in Computer Science, Statistics, Mathematics, Economics, Applied Data Science, Machine Learning, or a closely related quantitative field—or equivalent practical experience.
• Experience articulating business and product questions and translating them into analytical problems.
• Strong proficiency in SQL and at least one statistical programming language such as R, Python, SAS, Stata, or MATLAB.
• Hands-on experience with data analysis through internships, research, or professional roles.
• Solid understanding of experimental design, A/B testing, and statistical inference.

Preferred Qualifications

• Experience with Bayesian hierarchical models, Bayesian Additive Regression Trees (BART), Bayesian Causal Forest (BCF), Gaussian Processes, and Synthetic Control methods.
• Experience designing and analyzing randomized controlled trials at scale.
• Experience designing and analyzing causal research using observational data, including techniques for handling confounding, selection bias, and treatment heterogeneity.
• Deep expertise in statistical data analysis, including linear and generalized linear models, multivariate analysis, stochastic models, and modern sampling techniques.
• Track record of presenting analytical work to executive and non-technical audiences.
• Publications, conference talks, or open-source contributions in causal inference, econometrics, or applied statistics are a strong plus.

Skills and Competencies for Success

• Research Design: You know how to design a study that answers the question being asked—not just the question that is convenient to answer.
• Statistical Depth: You are comfortable reasoning about assumptions, biases, variance, and uncertainty. You can defend your methods and acknowledge their limits.
• Business Acumen: You care about the decision the analysis is meant to inform, not just the technical elegance of the model.
• Communication: You can write a clear narrative around a complex analysis and present it confidently to executives.
• Curiosity and Humility: You are always learning, always questioning, and always open to being wrong in service of getting it right.

Compensation and Benefits

The US base salary range for this full-time position is $124,000–$182,000, plus bonus, equity, and a comprehensive benefits package. Specific compensation will be determined based on the role, level, and work location, and will reflect the candidate’s job-related skills, experience, and relevant education or training. Your careerzynith recruiter will share the precise range for your location during the hiring process.

Careerzynith offers a robust benefits package designed to support your health, financial well-being, and personal growth, including but not limited to:

• Comprehensive medical, dental, and vision coverage
• Generous paid time off, holidays, and parental leave
• Retirement savings plans with company matching
• Equity participation for eligible roles
• Annual learning and development stipends
• Mental health and wellness resources

Please note that the compensation figures listed reflect base salary only and do not include bonus, equity, or additional benefits. Full benefits information will be provided during the recruitment process.

Why Work at careerzynith

At careerzynith, you will be part of a team that is serious about measurement and serious about impact. You will work on problems that genuinely matter to creators, audiences, and the future of digital media. You will be surrounded by people who love this craft, who will challenge your assumptions, and who will celebrate your breakthroughs.

We believe great work happens when people are trusted, supported, and given room to grow. Our culture emphasizes ownership, transparency, and a healthy respect for the difference between a good answer and a useful one.

How to Apply

If you are a seasoned data scientist with a passion for causal inference, a track record of influencing business decisions, and a desire to do meaningful work at scale, we would love to hear from you. Please submit your resume and a brief description of a research project you are proud of. Apply now to join careerzynith and help shape the future of impact measurement.