Principal Data Scientist - Advanced Analytics

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.

How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
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