The Data Science Manager will lead, scale, and operationalize Data Science, Artificial Intelligence (AI), Machine Learning (ML), and Generative AI (GenAI) solutions across enterprise applications. This role combines people leadership, technical strategy, and solution architecture, ensuring that AI initiatives move from experimentation to reliable, scalable, and value-generating production systems. The Data Science Manager will partner closely with business leaders, product teams, engineering, and platform teams to drive adoption of AI at scale while maintaining strong technical rigor and governance.

Responsibilities:

Lead and manage a team of data scientists, providing technical mentorship, career development, performance management, and delivery oversight Provide detailed guidance to the team for the development of Gen AI solutions and what all services should be delivered to deliver the functionality. Develop a high-level architecture of the required services and work with team to develop the solution. Preferred cloud providers (Azure, GCP). Experienced in combining services such as Microsoft Foundry, Azure APIM to develop Gen AI solutions that can scale at an enterprise level and used by 5000+ users making calls and retrieving information from Azure AI search at the same time. Own the end-to-end lifecycle of AI/ML and GenAI solutions — from ideation and experimentation to enterprise-scale deployment and ongoing optimization Partner with senior business stakeholders to identify high-impact opportunities, translate business problems into scalable analytical and AI solutions, and influence data-driven decision-making Define and drive the technical strategy for scalable AI/ML platforms, including model architecture, data pipelines, MLOps/LLMOps practices, and cloud deployment patterns Collaborate with Data Engineering, Platform, and Architecture teams to design robust, reusable, and secure data and AI infrastructure Establish and govern standards, best practices, and reusable frameworks for modeling, experimentation, deployment, monitoring, and responsible AI Oversee solution delivery across multiple concurrent initiatives, balancing speed, quality, risk, and long-term scalability Act as a technical escalation point and solution reviewer, ensuring architectural soundness, model performance, and operational readiness Drive measurable business outcomes through AI adoption, operational efficiency, automation, and advanced analytics Influence change at all levels of the organization, bridging technical depth with executive-level communication

Job Requirement:

Bachelor's Degree – Mathematics, Statistics, Data Science, Computer Science, Engineering, or related field Licenses/Certificates – Data Science, AI/ML (where applicable) Master's Degree – Data Science, AI/ML, Business Analytics, Computer Science Advanced certifications in AI/ML, Cloud Architecture, or MLOps/LLMOps

Qualifications:

10+ years of experience in Data Science, Analytics, or Applied AI, including 5+ years in a leadership or people-management role Proven experience scaling AI/ML and GenAI solutions from POC to enterprise production, similar to an AI Architect or Platform Lead Strong hands-on background in ML, statistical modeling, and advanced analytics, with the ability to guide technical decisions and review designs (hands-on coding a plus, not mandatory) Experience building and deploying ML/AI solutions across use cases such as classification, clustering, regression, NLP, time series, recommender systems, and optimization Deep understanding of MLOps and LLMOps, including CI/CD for models, experiment tracking, model monitoring, drift detection, observability, and governance Experience with distributed data and compute platforms (e.g., Azure Databricks, Spark, cloud-native data services, SQL/NoSQL databases) Experience with deep learning frameworks (TensorFlow, PyTorch, Keras) and modern GenAI stacks, including LLM deployment, prompt management, vector databases, and retrieval-augmented generation (RAG) Strong architectural mindset for designing secure, scalable, cost-efficient AI systems in the cloud Demonstrated ability to lead cross-functional teams and align data science efforts with product, engineering, and business roadmaps Excellent communication and storytelling skills, capable of engaging both executive stakeholders and technical teams Strong program and delivery management skills across multiple initiatives with competing priorities Experience operating under ambiguity and making decisions in complex, fast-changing environments

Note:
This role is designed for leaders who can bridge strategy, architecture, and execution—owning AI outcomes at scale while maintaining strong technical credibility.

 

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