Data Scientist

Responsibilities

• Advance AI capabilities by designing, developing, and deploying Generative AI solutions, including large language model (LLM) fine-tuning, prompt engineering, retrieval-augmented generation (RAG) pipelines, agentic workflows, and integration of AI into existing science and measurement workflows.
• Lead the end-to-end development and scaling of data science solutions from research and experimentation through deployment and product ionization.
• Ensure data science solutions are robust, maintainable, reproducible, and aligned with software engineering best practices.
• Partner with product managers and cross-functional stakeholders to define vision, roadmap priorities, and science product strategies within the personalization and loyalty domain.
• Contribute to the development of a unified and scalable science framework by connecting and consolidating existing science capabilities.
• Apply and expand causal machine learning and econometric methodologies, including CATE, difference-in-differences (DiD), matching techniques, panel methods, and heterogeneous treatment effect modeling.
• Support experimentation, measurement, and personalization initiatives through advanced statistical and machine learning approaches.
• Build, maintain, and optimize production machine learning and experimentation pipelines.
• Utilize MLOps and software engineering practices, including CI/CD, version control, testing, workflow management, monitoring, and documentation.
• Research and evaluate emerging AI and machine learning technologies to identify opportunities for innovation and adoption.
• Serve as a technical leader and subject matter expert by providing guidance and informal mentorship to team members.
• Communicate complex technical concepts, methodologies, and findings effectively to both technical and non-technical audiences, including leadership and business stakeholders.

Requirements

• Education Requirements: Bachelor’s or master’s degree in Statistics, Data Science, Computer Science, Applied Mathematics, Economics, or a related quantitative field.
• Experience Requirements: 3+ years of applied data science experience with progressive responsibility and increasing technical complexity. Hands-on experience developing and implementing Generative AI applications. Experience contributing to production-quality machine learning systems using software engineering best practices. Experience partnering with product managers and stakeholders to translate business requirements into data science solutions and strategic priorities. Experience working with Azure, Databricks, or comparable cloud-based data science platforms.

Skills

• Strong proficiency in Python, SQL, and Git.
• Hands-on experience with one or more Generative AI technologies, including LLM fine-tuning, prompt engineering, retrieval-augmented generation (RAG), or agentic workflow development.
• Familiarity with causal machine learning and causal inference methodologies, including CATE, heterogeneous treatment effect modeling, difference-in-differences (DiD), matching techniques, and related approaches.
• Experience developing scalable machine learning solutions from research through production deployment.
• Knowledge of cloud-based data science platforms such as Azure and Databricks.
• Understanding software engineering best practices for machine learning applications.
• Able to translate business challenges into analytical and scientific solutions.
• Strong verbal and written communication skills with the ability to communicate effectively across technical and business audiences.
• Able to work effectively in ambiguous and evolving environments while contributing to early-stage strategy and vision.
• Strong analytical, problem-solving, and critical-thinking capabilities.

Preferred Skills

• Experience with MLOps practices, including workflow orchestration, model monitoring, reproducibility, deployment, and operationalization.
• Familiarity with experimentation frameworks and measurement pipelines.
• Demonstrated ability to mentor, coach, or guide peers on technical best practices.
• Experience supporting personalization, loyalty, experimentation, or measurement-focused initiatives.