The Clorox Company
Overview
As a Data Scientist in Marketing at Clorox, you will apply data science techniques across our portfolio of brands to develop novel tools and analyses, support building data products, and translate data into clear, compelling insights that drive business impact. Responsibilities
Contribute to development and deployment of end-to-end AI/ML/Gen AI models focused on marketing, measurement, and predictive analytics. Explore, test, and implement GenAI solutions such as LLMs, embeddings, and 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. Collaborate closely with data engineers to ensure data quality, feature engineering, and efficient data pipelines. Act as a strategic partner to business stakeholders, framing 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. What we look for
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. Technical Skills
Programming: Proficiency in Python and SQL. AI/ML Expertise: Experience developing and deploying machine learning and generative AI (e.g., LLMs, embeddings, RAG pipelines) models; skilled in predictive modeling, personalized marketing, or customer analytics use cases. Data Handling: Comfortable working with unstructured/messy data; skilled in data exploration, cleaning, and feature engineering. Tools & Technologies: 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. Analytical & Strategic Skills
Ability to translate business problems into data science solutions and scope data projects with measurable impact. Comfortable leading discovery sessions and contributing strategically to cross‑functional teams. Skilled in data storytelling and communicating findings clearly through visualizations and business context. Soft Skills
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. Open to learning new tools and methods; adaptable to fast‑changing environments. Demonstrates business acumen. Workplace type
Hybrid role – 3 days in our office in the US.
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As a Data Scientist in Marketing at Clorox, you will apply data science techniques across our portfolio of brands to develop novel tools and analyses, support building data products, and translate data into clear, compelling insights that drive business impact. Responsibilities
Contribute to development and deployment of end-to-end AI/ML/Gen AI models focused on marketing, measurement, and predictive analytics. Explore, test, and implement GenAI solutions such as LLMs, embeddings, and 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. Collaborate closely with data engineers to ensure data quality, feature engineering, and efficient data pipelines. Act as a strategic partner to business stakeholders, framing 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. What we look for
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. Technical Skills
Programming: Proficiency in Python and SQL. AI/ML Expertise: Experience developing and deploying machine learning and generative AI (e.g., LLMs, embeddings, RAG pipelines) models; skilled in predictive modeling, personalized marketing, or customer analytics use cases. Data Handling: Comfortable working with unstructured/messy data; skilled in data exploration, cleaning, and feature engineering. Tools & Technologies: 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. Analytical & Strategic Skills
Ability to translate business problems into data science solutions and scope data projects with measurable impact. Comfortable leading discovery sessions and contributing strategically to cross‑functional teams. Skilled in data storytelling and communicating findings clearly through visualizations and business context. Soft Skills
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. Open to learning new tools and methods; adaptable to fast‑changing environments. Demonstrates business acumen. Workplace type
Hybrid role – 3 days in our office in the US.
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