About Company:

At Delaplex, we believe true organizational distinction comes from exceptional products and services. Founded in 2008 by a team of like-minded business enthusiasts, we have grown into a trusted name in technology consulting and supply chain solutions. Our reputation is built on trust, innovation, and the dedication of our people who go the extra mile for our clients. Guided by our core values, we don’t just deliver solutions, we create meaningful impact.

ROLE OVERVIEW

The Applied AI / Data Science Lead will provide hands-on execution capacity across data science and generative AI engineering. The role works closely with business product owners, data and technology teams, AI platform partners, and Responsible AI and risk stakeholders to shape use cases, build solutions, establish evaluation methods, and support the path from experimentation to production. This is a senior professional individual contributor role — someone who can independently lead complex technical work, make sound modeling and design decisions, and communicate trade-offs clearly to stakeholders at multiple levels.

RESPONSIBILITIES
• Work with business leaders and product owners to identify, assess, and shape high-value data science and AI opportunities across the General Bank, Commercial Bank, and Enterprise Functions.
• Translate business questions into well-defined analytical problem statements, with clear success measures, data requirements, solution hypotheses, implementation considerations, and expected value outcomes.
• Assess whether a problem is best addressed through conventional analytics, statistical modeling, machine learning, generative AI, workflow change, or no AI solution at all — and recommend a fit-for-purpose approach grounded in evidence and practicality, not novelty.
• Support prioritization of AI use cases by evaluating business value, data readiness, implementation feasibility, risk and control implications, operating model requirements, and the ability to measure impact over time.

Solution Design and Hands-On Development
• Design, build, validate, and refine analytical and AI solutions using appropriate methods: predictive modeling, supervised and unsupervised machine learning, natural language processing, generative AI, retrieval-augmented generation, optimization, or other advanced analytics techniques — selected on the basis of fit, not fashion.
• Develop data pipelines, features,



model prototypes, prompt or retrieval configurations, evaluation datasets, reusable code assets, and supporting documentation required for experimentation and responsible implementation.
• Establish transparent baselines and, where warranted, challenger approaches so that solution complexity is justified by measurable performance improvement or business value — not technical preference alone.
• Contribute technical judgment on model selection, vendor capabilities, enterprise platform services, solution architecture, integration needs, and production-readiness considerations.

Evaluation, Measurement, and Responsible Delivery
• Define and execute fit-for-purpose evaluation plans covering model performance, stability, interpretability, robustness, data quality, user acceptance, operational feasibility, monitoring, and business outcome measurement as appropriate to each use case.
• For generative AI solutions, develop evaluation approaches for task accuracy, groundedness and faithfulness, retrieval quality, human review effectiveness, harmful output risk, prompt handling, and other use-case-specific performance and control requirements.
• Partner with Responsible AI, model risk, business risk, compliance, legal, cybersecurity, privacy, and other stakeholders to ensure solutions are developed with appropriate documentation, testing evidence, controls, and ongoing monitoring plans from the start — not retrofitted at the end.
• Clearly communicate model assumptions, limitations, trade-offs, risks, recommended controls, and decision implications to business and technical stakeholders in language that is accessible, not just technically accurate.

Enterprise AI Platform and Reusable Capabilities
• Work alongside AI platform and technology partners as the bank's enterprise AI capabilities mature — providing practical requirements from data science delivery and positioning solutions to leverage approved platform services when ready.
• Develop reusable design patterns, evaluation methods, templates,



code assets, and delivery best practices that help the bank build AI solutions more consistently, securely, and efficiently over time.
• Support technical evaluation of AI tools, technologies, and vendors through objective testing and structured assessment of their relevance to business needs, enterprise architecture, and responsible adoption requirements.
• Contribute to experimentation and implementation pathways that connect enterprise data, AI models, monitoring, governance evidence, and operational workflows — building the infrastructure for AI at scale, not just one-off solutions.

Stakeholder Partnership and Technical Leadership
• Collaborate across business, data, technology, architecture, platform, and risk teams to move use cases from early ideas to disciplined experimentation and appropriate implementation — navigating complexity without losing momentum.
• Present analytical findings, solution alternatives, technical recommendations, risks, and outcomes in clear language for senior partners and decision makers who may not have a technical background.
• Share knowledge, mentor less experienced analysts through project delivery, and contribute to a team culture built on curiosity, craft, rigor, and honest evaluation of what is working and what is not.
• Stay current with meaningful developments in AI, ML, GenAI, and advanced analytics while maintaining a pragmatic focus: understanding what is actually ready for enterprise adoption versus what is still better suited to a research paper.

WHAT SUCCESS LOOKS LIKE
• High-value business problems are translated into sound analytical or AI use cases with transparent success measures and realistic implementation paths — including a clear view of what "good enough" looks like and when more complexity is not warranted.
• Solutions use the right technique for the problem. Model complexity and generative AI are justified by evidence of better outcomes, not by the availability of new technology.
• Experiments are designed rigorously, documented clearly, and positioned for responsible implementation through genuine partnership with platform, technology, and risk teams.
• Reusable analytical patterns, evaluation methods, and delivery assets help First Citizens increase speed, consistency, and trust as the bank builds its AI capability over time.

📌 Applied AI / Data Science Lead (Bengaluru)
🏢 delaPlex
📍 Bengaluru