Data Scientist Level 2

Job Description

Job Description

Company Overview
84.51 knows customers, and we know how to connect you.
Using a sophisticated, proprietary suite of tools and technology, we turn customer data into actionable knowledge. With unparalleled customer data and predictive analytics capabilities, we deliver personalized marketing strategies and ensure the best experience for customers of and more than 300 consumer-packaged-goods companies. We put the customer at the center of everything we do, resulting in a more dynamic, informed and personal approach to driving customer loyalty.

Summary
The Personalization and Loyalty team's mission is to make the destination of choice for our customers. We drive loyalty by delivering what our customers wantat the right time, with the right valuecreating meaningful connections that deepen their relationship with . As part of this organization, the KM+ DSR team applies statistical science, causal inference, and AI to design experiments, measure impact, and scale insights that drive customer value and loyalty.
We're seeking a Data Scientist to help shape the future of our AI and science capabilities. This is a senior individual contributor role for a technically strong, forward-thinking data scientist who can advance our Gen AI and causal ML capabilities, lead end-to-end development of scalable science solutions, and partner with product and cross-functional teams to drive vision and strategy in our space.

Qualifications, Skills & Experience
3+ years of applied data science experience, with demonstrated progression in scope and technical complexity
Hands-on experience with Generative AI applications, including one or more of: LLM fine-tuning, prompt engineering, RAG pipelines, or agentic workflow development
Familiarity with causal ML and/or causal inference methods (e.g., CATE, heterogeneous treatment effect modeling, DiD, matching)
Strong proficiency in Python, SQL, and Git
Experience with Azure and Databricks, or comparable cloud-based data science platforms
Experience contributing to production-quality ML systems using software engineering best practices
Ability to partner with product managers and stakeholders to translate business needs into science solutions and roadmap priorities
Strong oral and written communication skills, with the ability to translate between technical and business audiences
Comfort with ambiguityable to operate effectively in evolving problem spaces and contribute to early-stage vision and strategy
Bachelor's or Master's in Statistics, Data Science, Computer Science, Applied Math, Economics, or related quantitative field

Preferred:
Experience with ML Ops practices including workflow orchestration, model monitoring, reproducibility, and deployment
Experience in retail, CPG, media, or marketplace analytics
Demonstrated ability to informally mentor or coach peers in technical best practices
Familiarity with experimentation frameworks and measurement pipelines

Key Responsibilities
Responsibilities
Advance our AI capabilities by designing, developing, and deploying Gen AI solutionsincluding LLM fine-tuning, prompt engineering, RAG pipelines, agentic workflows, and integration of Gen AI into existing measurement and science workflows.
Lead end-to-end development and scaling of data science solutions, from research and experimentation through productionization, ensuring solutions are robust, reproducible, and maintainable.
Partner with product managers and cross-functional stakeholders to shape the vision, roadmap, and prioritization of science products and capabilities in the personalization and loyalty space.
Contribute to the vision and early development of a holistic science layerworking to connect and consolidate scattered science capabilities into a unified, scalable framework.
Apply and extend causal ML and econometric methods (e.g., CATE, DiD, matching, panel methods) to support measurement, experimentation, and personalization at scale.
Build, maintain, and improve production ML and experimentation pipelines using sound MLOps and software engineering practices, including CI/CD, version control, testing, and documentation.
Research and evaluate emerging AI/ML technologies and methodologies, identifying opportunities to bring state-of-the-art approaches into production.
Serve as a technical leader and subject matter expert on the team, providing guidance and informal mentorship to peers and evolving into a formal mentor as junior talent joins the team.
Communicate complex technical findings and methodologies clearly to both technical and non-technical audiences, including leadership and product stakeholders.