Data Scientist - Marketing

About the Role

Key Responsibilities

• Contribute to the development and deployment of end-to-end AI/ML/Gen AI models, focusing on marketing, measurement, and predictive analytics.
• Explore, test, and implement GenAI solutions (e.g., LLMs, embeddings, RAG pipelines) for use cases like personalized marketing, customer insights, or content generation.
• Scope and define data science projects, set clear deliverables, and collaborate with stakeholders to develop and deploy impactful models.
• Write clean, well-organized code in GitHub repositories, following version control and collaboration best practices for development with other team members.
• Collaborate closely with data engineers to ensure data quality, feature engineering, and the development of efficient data pipelines.
• Act as a strategic partner to business stakeholders, helping them frame problems in a data-driven way and translating business goals into analytical solutions.
• Lead discovery sessions with marketing, product, and leadership teams to understand pain points and identify opportunities where data science can create value.
• Present analytical findings and model results through compelling storytelling, using visualizations and business context to drive alignment and decision-making.

Requirements

• Bachelor's degree or higher in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field. CPG experience is preferred.
• 1+ years of relevant experience with a Master's degree, or 2-3+ years of experience with a Bachelor's degree.
• Proven experience working with business stakeholders; business-facing or consulting experience is preferred.
• Proficiency in Python and SQL.
• Experience developing and deploying machine learning and generative AI models (e.g., LLMs, embeddings, RAG pipelines).
• Skilled in predictive modeling, personalized marketing, or customer analytics use cases.
• Comfortable working with unstructured/messy data, and skilled in data exploration, cleaning, and feature engineering.
• Familiarity with GitHub and collaborative version control best practices.
• Experience with cloud platforms such as Google Cloud (BigQuery, GCS) or Azure Synapse is a plus.
• Knowledge of Docker and cloud-based deployment is an advantage.
• Ability to translate business problems into data science solutions and scope data projects with measurable impact.
• Strong communication and collaboration skills; able to work with both technical and non-technical stakeholders.
• Highly curious, self-motivated, and able to work independently.
• Strong project management and time management capabilities.

Nice to Have

• Experience with cloud platforms such as Google Cloud (BigQuery, GCS) or Azure Synapse.
• Knowledge of Docker and cloud-based deployment.

Qualifications

• Bachelor's degree or higher in a relevant field.
• 1+ years of experience with a Master's degree, or 2-3+ years with a Bachelor's degree.

Benefits & Perks

• Comprehensive, competitive benefits that prioritize all aspects of wellbeing.
• Robust health plans.
• Market-leading 401(k) program with a company match.
• Flexible time off benefits, including half-day summer Fridays depending on location.
• Inclusive fertility/adoption benefits.
• Transparent salary ranges with opportunities for growth based on experience and responsibilities.
• Participation in incentive plans, subject to plan terms.