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Director | Managed Analytics, AI Services & DEX | Bengaluru | NAT : Innovation
senior · Data Science & Analytics
Director | Managed Analytics, AI Services & DEX | Bengaluru | NAT : Innovation
• Job requisition ID : 112030
• Location: Bengaluru
• Entity: Deloitte Touche Tohmatsu India LLP
The team
The team must do much more than keep the wheels turning; it is the engine that drives functional excellence and the enabler of innovation and long-term growth.
Key Responsibilities
- Define and execute the organization's Data Science and AI strategy in alignment with business objectives, product vision, and the broader technology strategy.
- Lead, build, and mentor high-performing Data Science and Machine Learning teams, while developing strong technical leaders and establishing a culture of innovation, scientific rigor, experimentation, and continuous learning.
- Own the end-to-end data modelling strategy across intelligent products, ensuring modelling approaches are statistically sound, scalable, explainable, reliable, and continuously improving.
- Define and drive the Machine Learning roadmap across supervised and unsupervised learning, deep learning, graph learning, time-series forecasting, generative AI, predictive analytics, recommendation systems, and decision intelligence.
- Guide teams in selecting appropriate algorithms, feature engineering approaches, modelling techniques, evaluation methodologies, and deployment strategies to deliver production-grade AI solutions.
- Establish enterprise-wide standards and governance for model development, validation, deployment, monitoring, versioning, explainability, retraining, and retirement.
- Lead the development of responsible and trustworthy AI practices, ensuring models are accurate, fair, explainable, reliable, secure, auditable, and compliant with applicable regulatory requirements.
- Champion the use of AI-powered tools as a force multiplier across Data Science workflows, including data exploration, feature engineering, experimentation, statistical analysis, model development, documentation, and knowledge discovery.
- Ensure AI-assisted outputs and models are scientifically valid, reproducible, explainable, and aligned with business and customer outcomes.
- Drive the development of intelligent financial services capabilities across areas such as credit underwriting, credit risk assessment, fraud detection, customer intelligence, portfolio monitoring, early warning systems, collections optimization, cash flow forecasting, decision intelligence, predictive analytics, recommendation engines, and AI-powered decision support.
- Provide strategic leadership for credit risk modelling, including Probability of Default, behavioral scorecards, risk segmentation, portfolio analytics, and predictive risk models.
- Establish reusable feature engineering, modelling, experimentation, and analytical frameworks that accelerate the development and deployment of intelligent products across the organization.
- Partner closely with Product Management, Engineering, Architecture, MLOps, Security, Experience Design, Business, and Risk teams to embed Data Science throughout the product development lifecycle.
- Establish strong collaboration between Data Science and Engineering teams to ensure models are production-ready, scalable, observable, maintainable, and capable of continuous learning.
- Drive adoption of MLOps practices covering feature stores, model registries, experiment tracking, model versioning, deployment automation, model monitoring, drift detection, and automated retraining.
- Establish model review and independent validation processes to continuously assess model performance, stability, accuracy, fairness, explainability, and business impact.
- Translate complex business and customer problems into data-driven solutions and intelligent product capabilities that deliver measurable business value.
Identify emerging AI and Data Science technologies and determine how they can be responsibly adopted to improve product capabilities, team productivity, and competitive differentiation.
Key Requirements
- 12+ years of experience in Data Science, Machine Learning, Artificial Intelligence, Statistical Modelling, or related fields, with significant experience leading Data Science organizations.
- Proven experience building and leading high-performing Data Science and Machine Learning teams that deliver production-grade AI and ML solutions at enterprise scale.
- Strong expertise in Statistics, Probability, Machine Learning, Optimization, Predictive Analytics, Experimental Design, and Statistical Modelling.
- Strong proficiency in Python and hands-on experience with modern machine learning frameworks and technologies such as Scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch, or equivalent platforms.
- Strong experience in designing enterprise-scale machine learning solutions and translating analytical models into scalable production systems.
- Deep understanding of feature engineering, model development, model evaluation, model validation, model explainability, model monitoring, and model governance.
- Strong understanding of MLOps practices and experience working with feature stores, model registries, experiment tracking, model deployment, monitoring, model versioning, and ML lifecycle management.
- Strong SQL skills with experience working with large-scale datasets and modern data processing platforms.
- Strong understanding of Responsible AI principles, including fairness, bias mitigation, explainability, transparency, reproducibility, privacy, security, and regulatory compliance.
- Experience applying AI-powered tools to accelerate Data Science research, experimentation, feature engineering, model development, documentation, and knowledge discovery.
- Strong financial services domain knowledge, preferably with experience in lending, credit risk, underwriting, fraud, portfolio management, or financial decisioning.
- Preferred experience in Probability of Default modelling, behavioral scorecards, credit risk modelling, credit underwriting, and portfolio risk analytics.
- Working knowledge of advanced areas such as Graph Machine Learning, Time Series Forecasting, Causal Inference, Bayesian Statistics, Reinforcement Learning, Explainable AI, Knowledge Graphs, Generative AI, and Decision Intelligence.
- Strong understanding of AI and Model Governance frameworks and experience establishing enterprise standards for model risk management and validation.
- Strong product mindset with the ability to translate business problems into scalable data-driven and AI-powered product solutions.
- Excellent leadership, communication, stakeholder management, influencing, and cross-functional collaboration skills, with the ability to communicate complex technical concepts effectively to senior business and technology leaders.