Senior Data Scientist (AI ML)

As a Senior Data Scientist specializing in AI/ML, your role will involve developing, validating, and deploying machine learning and generative AI solutions to optimize omnichannel communications for marketing and advertising. You will collaborate with business stakeholders and engineering teams to translate use cases into scalable models, establish MLOps pipelines, and continuously enhance model performance for tangible business impact. Key Responsibilities: - Partner with business stakeholders to identify, prioritize, and scope data science initiatives that drive measurable marketing/advertising value, including GenAI-driven opportunities. - Design, build, validate, and productionize supervised and unsupervised ML models and generative AI solutions for optimizing omnichannel communications and customer engagement. - Implement MLOps best practices such as CI/CD for models, containerized deployments, monitoring, drift detection, and model refresh workflows. - Develop and maintain robust data pipelines, feature engineering processes, and model validation/testing frameworks. - Utilize modern GenAI techniques like embeddings, RAG, prompt engineering to create scalable solutions for content personalization, summarization, intent detection, or other use cases. - Conduct exploratory data analysis, implement quality checks, and generate actionable insights and KPIs for stakeholders. - Collaborate with cross-functional teams to review modeling work, integrate models into platforms, and ensure operational readiness. - Monitor model performance post-deployment, iterate on models and thresholds, and document model lineage and assumptions. - Produce comprehensive technical and non-technical documentation and present results to internal and external partners. Qualifications: - Advanced degree (Masters or PhD) in statistics, mathematics, economics, computer science, or a related quantitative field. - Demonstrated experience in applying ML/Deep Learning and proven track record with generative AI/LLMs in business contexts. As a Senior Data Scientist specializing in AI/ML, your role will involve developing, validating, and deploying machine learning and generative AI solutions to optimize omnichannel communications for marketing and advertising. You will collaborate with business stakeholders and engineering teams to translate use cases into scalable models, establish MLOps pipelines, and continuously enhance model performance for tangible business impact. Key Responsibilities: - Partner with business stakeholders to identify, prioritize, and scope data science initiatives that drive measurable marketing/advertising value, including GenAI-driven opportunities. - Design, build, validate, and productionize supervised and unsupervised ML models and generative AI solutions for optimizing omnichannel communications and customer engagement. - Implement MLOps best practices such as CI/CD for models, containerized deployments, monitoring, drift detection, and model refresh workflows. - Develop and maintain robust data pipelines, feature engineering processes, and model validation/testing frameworks. - Utilize modern GenAI techniques like embeddings, RAG, prompt engineering to create scalable solutions for content personalization, summarization, intent detection, or other use cases. - Conduct exploratory data analysis, implement quality checks, and generate actionable insights and KPIs for stakeholders. - Collaborate with cross-functional teams to review modeling work, integrate models into platforms, and ensure operational readiness. - Monitor model performance post-deployment, iterate on models and thresholds, and document model lineage and assumptions. - Produce comprehensive technical and non-technical documentation and present results to internal and external partners. Qualifications: - Advanced degree (Masters or PhD) in statistics, mathematics, economics, computer science, or a related quantitative field. - Demonstrated experience in applying ML/Deep Learning and proven track record with generative AI/LLMs in business contexts.