Marketing Data Science Manager

We are seeking a skilled and versatile Data Science Manager with AI familiarity to join our growing team. In this role, you’ll collaborate with practice leaders, engineers, and cross-functional stakeholders to solve complex business challenges using data science and AI-driven approaches. You’ll work on end-to-end data science initiatives, with opportunities to design and implement cutting-edge generative AI (GenAI) and LLM-powered solutions.

Key Responsibilities

Data Science & Analytics

• Partner with practice leaders and clients to understand business problems, industry context, data sources, risks, and constraints.

• Translate business needs into actionable data science solutions, evaluating multiple approaches and clearly communicating trade-offs.

• Collaborate with stakeholders to align on methodology, deliverables, and project roadmaps.

• Leverage Machine Learning and Data Analysis to optimize marketing campaigns

• Conduct A/B tests to improve campaign performance measure campaign effectiveness, and increase engagement and conversion rates. 

AI & Generative AI Collaboration

In addition to traditional data science responsibilities, you will collaborate with AI and engineering teams to:

• Design and implement production-grade AI solutions leveraging LLMs, transformers, retrieval-augmented generation (RAG), agentic workflows, and generative AI agents.

• Optimize prompt design, workflows, and pipelines for performance, accuracy, and cost-efficiency.

• Build multi-step, stateful agentic systems that utilize external APIs/tools and support robust reasoning.

• Deploy GenAI models and pipelines in production (API, batch, or streaming) with a focus on scalability and reliability.

• Develop evaluation frameworks to monitor grounding, factuality, latency, and cost.

• Implement safety and reliability measures such as prompt-injection protection, content moderation, loop prevention, and tool-call limits.

• Work closely with Product, Engineering, and ML Ops to deliver robust, high-quality AI capabilities end-to-end.

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• Develop and manage detailed project plans including milestones, risks, owners, and contingency plans.

• Create and maintain efficient data pipelines using SQL, Spark, and cloud-based big data technologies within client architectures.

• Collect, clean, and integrate large datasets from internal and external sources to support functional business requirements.

• Build analytics tools that deliver insights across domains such as customer acquisition, operations, and performance metrics.

• Perform exploratory data analysis, data mining, and statistical modeling to uncover insights and inform strategic decisions.

• Train, validate, and tune predictive models using modern machine learning techniques and tools.

• Document model results in a clear, client-ready format and support model deployment within client environments.