Sr. Manager, Data Science & Applied AI

About the job

Your New Role: As the Sr. Manager, Data Science & Applied AI, you will play a pivotal role in driving the implementation of impactful AI/ML solutions across the enterprise. You will be responsible for managing a high-performing team of data scientists and machine learning engineers, delivering solutions that enhance operational efficiency, audience engagement, and decision intelligence in the Media & Entertainment ecosystem. This is a strategic and hands-on leadership position focused on developing cutting-edge ML/AI products—ranging from predictive models and recommender systems to generative AI and computer vision use cases—while ensuring robust production deployment and adoption. 1. End-to-End AI Solution Delivery & Technical Oversight - Lead the full lifecycle of AI/ML project delivery: from problem formulation and hypothesis generation to data acquisition, feature engineering, model development, deployment, and continuous optimization. - Design and review advanced statistical models, machine learning algorithms, and deep learning architectures for tasks such as customer segmentation, forecasting, personalization, natural language processing, image/video analysis, and generative content creation. - Ensure solutions are built for scale, performance, and robustness using modern engineering and MLOps practices—e.g., CI/CD pipelines for models, monitoring dashboards, automated retraining loops. - Guide model explainability, fairness, and compliance practices in line with Responsible AI guidelines. 2. Strategic Team Leadership & Capability Building - Manage, mentor, and grow a high-performing team of data scientists and ML engineers by defining clear roles, growth paths, and technical competencies. - Lead regular code reviews, model design sessions, and innovation sprints to ensure rigor, reusability, and excellence. - Foster a culture of curiosity, continuous learning, and business alignment, encouraging contributions to internal AI knowledge bases, innovation forums, and external conferences. - Define and implement team KPIs that measure both technical quality and business impact. 3. Business Problem Translation & Stakeholder Management - Act as a bridge between business challenges and AI solutions by deeply understanding domain pain points and framing them into solvable data science problems. - Partner with business stakeholders across functions (e.g., revenue, audience insights, ad sales, content strategy) to identify AI use cases with high ROI. - Develop compelling narratives and visualizations to communicate results and recommendations to non-technical stakeholders, including senior executives. - Regularly present outcomes, risks, and learnings to steering committees, ensuring transparency and strategic alignment. 4. AI Productization & Operationalization - Ensure that models are not just proof-of-concepts but are integrated into production systems with real-time or batch inference pipelines. - Design scalable APIs, model versioning, and deployment artifacts using best-in-class tooling (e.g., SageMaker, MLflow, Vertex AI, or Kubeflow). - Work with DevOps and platform teams to standardize model deployment workflows, automate drift detection, and reduce time-to-market for ML products. - Maintain a post-deployment monitoring framework to track model health, user feedback, and business performance indicators. 5. Governance, Risk Management & Ethical AI - Partner with Legal, Compliance, and Risk functions to implement guardrails for ethical AI use, especially in sensitive areas like user profiling or content moderation. - Establish frameworks for model documentation, reproducibility, audit trails, and change control. - Stay abreast of global AI regulations and proactively align team practices with internal and external compliance requirements.