AI Personalisation Product Lead

AI Personalisation Product Lead

• This is a deeply technical role. The ideal candidate has a background in machine learning engineering, data science leadership, or AI product management within a production ML environment.

• They must be fluent in the language of model training, feature engineering, serving infrastructure, latency optimisation, champion-challenger experimentation, and MLOps — not just able to discuss these topics at a conceptual level, but able to make architectural decisions, review model performance, challenge data science approaches, and unblock engineering bottlenecks.

• This person runs sprint planning, reviews pull requests with the team, triages production incidents, and makes trade-off decisions between model accuracy and serving latency.

• The role leads a technical team of 13 to 15 resources in data science, ML and engineering.

Key Responsibilities:

Next Best Action (NBA) Engine — Product Ownership

• Own the NBA product vision, technical roadmap, and engineering backlog — defining what capabilities the engine needs to develop, in what sequence, and to what performance standard.

• Define the NBA decision framework: what actions the engine evaluates, what signals it consumes, what constraints it respects (commercial rules, partner obligations, frequency caps, POPIA consent), and how it ranks competing actions.

• Direct the Data Science team on model development.

• Own the real-time serving infrastructure.

• Design and govern champion-challenger experimentation: define experiment protocols, statistical rigour standards, traffic allocation strategies, and decision criteria for model promotion.

• Build and maintain the feature store: curate the real-time and batch feature sets (behavioural, demographic, transactional, contextual) that feed NBA models, ensuring feature freshness, quality, and governance.

• Integrate NBA outputs into Rewards and Digital Platform customer touchpoints: work with Technology/Engineering on API design, integration patterns, fallback handling, and error management.

• Monitor NBA performance daily: model accuracy (AUC/precision/recall), acceptance rates, revenue attribution, latency metrics, and drift detection — triggering retraining when performance degrades.

• Build feedback loops: ensure action outcomes (accepted, ignored, rejected) are captured, pipeline-processed, and fed back into model training data on a defined cadence.

• Report NBA performance and attributed revenue to Business Owners monthly, with clear methodology transparency.

Content Personalisation Engine — Product Ownership

• Own the content personalisation product vision and technical strategy: how the engine determines which content, articles, offers, and recommendations to serve to each user.

• Direct the development of content recommendation algorithms: collaborative filtering, content-based filtering, hybrid approaches, and contextual re-ranking models.

• Build and maintain user interest profiles from behavioural data (content views, reads, dwell time, shares), stated preferences, and inferred affinities.

• Own the content taxonomy and tagging framework (jointly with Data Architect): define the metadata structure, classification schema, and machine-readable content attributes that enable effective algorithmic matching.

• Integrate personalised content feeds into Digital Platform (app, web, WhatsApp) and Rewards experiences.

• Implement editorial override and curation controls: build mechanisms that allow marketing and content teams to pin, boost, suppress, or schedule specific content alongside algorithmic recommendations.

• Design and run experiments to measure personalisation impact: A/B tests of personalised vs. non-personalised content, content diversity experiments, and filter bubble detection.

• Ensure content diversity to prevent filter bubbles: implement exploration mechanisms, serendipity algorithms, and diversity constraints that maintain content discovery alongside personalisation.

• Monitor content personalisation performance: engagement lift, content consumption depth, return frequency, and personalisation coverage metrics.

Dynamic Optimisation & Experimentation Infrastructure

• Own the experimentation platform: design, build, and maintain the A/B testing and multi-armed bandit infrastructure that enables automated optimisation of campaigns and experiences.

• Implement multi-armed bandit algorithms for automated traffic allocation: where experiments should converge to the winning variant dynamically rather than waiting for manual analysis.

• Build real-time campaign optimisation pipelines: automated bid, creative, and audience adjustment based on streaming performance signals.

• Develop adaptive algorithms for experience personalisation that improve over time through continuous learning — not just static rule-based personalisation.

• Create guardrails and circuit breakers: automated safety mechanisms that prevent optimisation algorithms from degrading experiences, violating business rules, or producing statistically invalid conclusions.

• Establish statistical rigour standards for experiment design: minimum sample sizes, power calculations, duration rules, and significance thresholds.

• Build dashboards that surface experiment results and automated decisions transparently: stakeholders must be able to see what the algorithms are doing and why.

Chatbot AI Personalisation — Product Ownership

• Own the chatbot personalisation product: the AI/ML capability that makes the chatbot contextually aware, brand-consistent, and able to guide users through personalised journeys.

• Integrate the chatbot with NBA engine outputs: the chatbot should be able to surface the NBA-recommended action within conversation, not operate as a disconnected system.

• Integrate the chatbot with the content personalisation engine: the chatbot should recommend relevant content, articles, and features based on the user's profile and conversation context.

Customer Micro-Segmentation — Technical Infrastructure

• Own the technical segmentation infrastructure: the models, pipelines, and data systems that produce granular customer segments for precision targeting and personalisation.

• Direct the Data Science team on segmentation model development.

• Build dynamic segments that update in real time as customer behaviour changes — not static segments refreshed weekly.

MLOps, Platform Engineering & Technical Governance

• Own the ML development lifecycle across all AI products: model training, validation, deployment, monitoring, retraining — with defined cadences, quality gates, and rollback procedures.

Required Skills & Experience

• BE/B Tech, BCA/MCA with 6+ Years experience as a background in machine learning engineering, data science leadership, or AI product management within a production ML environment.

• Ready to work in Bangalore / Pune / Hyderabad

• Ready to join within 15 days

• Tertiary qualification in Computer Science, Machine Learning, Data Science, Statistics, Engineering, or a related technical field. Postgraduate (MSc/PhD) in ML, AI, or statistical modelling strongly preferred.

• Minimum 7–10 years of experience in ML/AI product development, data science, or ML engineering, with at least 3–5 years in a product lead, tech lead, or senior data science leadership role.

• Deep technical fluency in machine learning: supervised and unsupervised learning, recommendation systems, NLP/NLU, reinforcement learning, multi-armed bandits, and real-time model serving. Must be able to review model architectures, challenge experimental designs, and make build-vs-buy technical decisions.

• Production ML experience: has built and operated ML systems that serve real-time decisions at scale (not just research/experimentation). Understands latency optimisation, feature store design, model drift, and serving infrastructure.

• Hands-on proficiency (current or recent) in Python, SQL, and at least one ML framework (PyTorch, TensorFlow, scikit-learn, XGBoost). Not expected to write production code daily, but must be able to read code, review PRs, and prototype approaches.

• Experience with MLOps practices: CI/CD for models, experiment tracking (MLflow, Weights & Biases), model monitoring, automated retraining pipelines, and deployment orchestration.

• Experience with real-time serving infrastructure: low-latency API design, feature serving, and integration with consumer-facing digital products.

• Experience leading cross-functional technical teams: data scientists, ML engineers, software engineers, QA, and solution architects working together on AI product delivery.

• Product management skills: ability to define product vision, manage a technical backlog, make prioritisation trade-offs, and communicate technical progress to non-technical Business Owners.

• Experience with experimentation and A/B testing at scale: champion-challenger design, statistical rigour, and multi-armed bandit deployment.

• Strong communication skills: ability to translate complex ML concepts into business impact language for executive and Business Owner audiences, and to write clear technical documentation for engineering teams.