Director - Artificial Intelligence

Location: Bengaluru, India; Mysuru, KA, IND, India

Department: MADTECH.AI

Experience: 12 to 20 Years

Skills: Multi-Touch Attribution (MTA), Artificial Intelligence, Marketing Mix Modeling (MMM), Data Science & Modeling, AWS, Python, Natural Language Processing (NLP)

• Design AI systems that move beyond descriptive insights to prescriptive and scenario-based decision intelligence.
• Architect and deploy enterprise-grade GenAI applications, including content intelligence, automated reporting, proactive campaign insights, and conversational analytics.
• Apply advanced NLP techniques such as sentiment analysis, topic modeling, entity extraction, and text classification to enhance marketing intelligence.
• Integrate LLM-driven intelligence into predictive workflows, automation systems, and decision-support platforms.
• Establish governance frameworks for Responsible AI, bias mitigation, explainability, and compliance.

• Provide executive oversight for development and deployment of:
• Marketing Mix Modeling (MMM)
• Multi-Touch Attribution (MTA)
• Propensity, RFM, and lifetime value modeling
• Bayesian and non-parametric modeling techniques
• Institutionalize experimentation frameworks (A/B testing, incrementality testing, statistical significance validation).
• Drive AI-led transformation of campaign performance measurement and marketing ROI attribution.
• Establish analytical best practices across the AI function.
• Develop vertical-specific AI accelerators and intelligence frameworks tailored to priority industry segments (e.g., nonprofit, higher education, healthcare, retail).

• Lead the development of predictive models to optimize:
• Revenue growth
• Audience behavior forecasting
• Bid strategy automation
• Inventory yield optimization
• Leverage campaign and media trading datasets to improve eCPM, fill rates, conversion rates, and ROI.
• Enable cross-channel optimization across display, video, mobile, social, native, advanced TV, and audio ecosystems.
• Build real-time or near-real-time intelligence layers to power automated decision-making.

• Architect scalable data platforms integrating audience, contextual, pricing, and transactional datasets.
• Define canonical marketing data models to enable cross-client benchmarking and collective intelligence frameworks.
• Lead AI-driven automation of connector discovery, schema mapping, anomaly detection, and intelligent data normalization to support scalable multi-source integrations.
• Oversee integrations with DSPs, SSPs, DMPs, CDPs, and other AdTech platforms.
• Drive adoption of robust MLOps frameworks, model lifecycle management, and continuous deployment pipelines.
• Establish enterprise-grade standards for data quality, governance, security, and system performance.
• Collaborate with engineering leadership on cloud architecture strategy (AWS/Azure/GCP).

• Build and scale a high-performing AI organization including Data Scientists, ML Engineers, Applied Researchers, and MLOps professionals.
• Mentor senior AI leaders and establish succession pipelines.
• Drive a culture of experimentation, innovation, accountability, and quality.
• Align AI initiatives with broader organizational OKRs and revenue strategy.

• Partner closely with Product, Engineering, Yield, Sales, Finance, and Operations teams to embed AI into core business workflows.
• Translate complex analytical outputs into executive-ready business insights.
• Engage with enterprise clients and strategic partners to showcase AI capabilities.
• Contribute to positioning MADTECH.AI as a market-leading AI-driven marketing intelligence platform.

• Master’s in Artificial Intelligence, Data Science, Computer Science, Statistics, or related field. Ph.D. preferred.
• 12+ years of experience in Data Science/AI, with 6+ years in leadership roles managing AI teams.
• Proven experience delivering AI-driven products in AdTech, MarTech, Media, or Digital Marketing ecosystems.
• Strong expertise in:
• Machine Learning & Deep Learning
• NLP & Large Language Models
• Advanced statistical modeling
• Cloud-native AI architecture
• Hands-on familiarity with Python, ML frameworks (TensorFlow, PyTorch), and distributed data systems.
• Experience implementing MLOps best practices in production environments.

• Strategic and commercial orientation.
• Strong decision-making and prioritization capability.
• Ability to balance innovation with scalable execution.
• Executive-level communication and stakeholder management skills.
• High ownership mindset with measurable impact focus.

• AI-driven revenue contribution and margin improvement.
• Improvement in campaign ROI, eCPM, fill rates, and attribution accuracy.
• Adoption rate of GenAI-powered features.
• Model deployment velocity and reliability.
• Team engagement, retention, and leadership pipeline strength.
• AI-attributed revenue growth and contribution to new ARR.
• Reduction in manual analytics effort and improvement in client retention through AI-enabled differentiation.