Marketing Data Science Manager

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.
• 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.
• Required Skills & Experience
• 5+ years of hands-on experience in Data Science, including model building and ML Ops
• Experience in email marketing and direct marketing
• Experience managing people
• Proficiency in Python , SQL , and tools like Pandas , Scikit-learn , NLTK / spaCy , and Spark
• Familiarity with digital marketing ecosystem (e.g., clickstream analytics) and recommendation systems
• Experience deploying models via APIs or integrating them into batch processing pipelines
• Working knowledge of cloud data platforms (e.g., AWS S3, Redshift, GCP, Azure)
• Ability to manage data pipelines and ETL processes with a solid understanding of data engineering best practices
• Strong communication and collaboration skills, including experience engaging directly with clients

Requirements

• Exposure to ML Ops tools such as MLflow , Kubeflow , or SageMaker
• Experience working in Agile environments with cross-functional teams

Benefits

Additional Information

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.

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