Ops Manager- Credit Analytics & Data

, India

Job Family Group:

Finance

Worker Type:

Regular

Posting Start Date:

April 21, 2026

Business Unit:

Finance

Experience Level:

Experienced Professionals

Job Description:

Purpose

Shell's Credit Data & Analytics (CDA) team is responsible for delivering high-quality credit risk analytics, reporting, and data solutions that support Shell's global trading and supply operations.

1. Team Leadership & People Management

• Lead, manage, and develop a high‑performing team of data and analytics professionals based in India, working in close partnership with the London‑based Credit Data & Analytics Manager.

• Drive a high-performance culture focused on quality, delivery, continuous improvement, and professional development.

• Build a sustainable, high‑performing team by actively developing, engaging, and retaining talent.

• Liaise with technology and analytics platform teams to support enhancements, adoption of new capabilities, and resolution of data or tooling issues affecting BAU delivery.

• Drive adoption of new analytics platform features as they are released, embedding them into BAU workflows and team ways of working.

2. CDA BAU Analytical Services

• Own and manage the delivery of Credit Data & Analytics BAU services from the India team, including regular credit risk reporting, counterparty exposure monitoring, portfolio dashboards, and ECL/CVA analytical outputs.

• Ensure all BAU deliverables are produced to the highest standards of accuracy, timeliness, and completeness, in line with agreed SLAs and Global Credit Services credit risk policy.

• Manage the analytical calendar, ensuring the team is resourced and prepared for period-end reporting cycles, ad hoc business requests, and regulatory submissions.

• Oversee the full lifecycle of analytical products – from data sourcing and transformation through to insight generation and presentation-ready outputs for senior stakeholders including the CRO.

• Drive continuous improvement in BAU processes, identifying opportunities to automate, streamline, and enhance analytical coverage using the CAI platform capabilities.

3. Analytical Solutions Development & AI/ML Innovation

• Champion the adoption of AI and machine learning techniques in BAU processes to enhance credit risk analytics – including predictive counterparty risk models, anomaly detection, scenario analysis, and NLP-driven data extraction.

• Oversee the use of Python-based ML frameworks (scikit-learn, XGBoost, or equivalent) and orchestration tools within the Azure environment to automate and enhance analytical pipelines.

• Evaluate and adopt emerging AI/ML and analytical tooling that can improve team productivity and analytical quality.

• Champion best-in-class engineering practices within the team – including code version control (GitHub/dbt), automated testing, peer review, and solution documentation.

• Support ad hoc modelling and analytical requests from trading desks, the CRO, and senior credit stakeholders, leveraging AI-enhanced methodologies where appropriate.

4. Stakeholder Engagement

• Serve as the primary operational interface between the SBO India CDA team and London-based senior stakeholders.

• Build trusted relationships with trading desks, risk management, IT delivery partners, and data platform teams across Shell's global credit organization.

• Represent the India team in governance forums, sprint reviews, and senior stakeholder updates, communicating delivery status, analytical insights, and team performance clearly.

• Proactively surface capacity risks, data quality issues, or delivery blockers to line management, with proposed mitigations.

5. Continuous Improvement

• Embed CDA’s new data platform (CAI) usage within the India team’s operating model, driving consistent adoption of Snowflake and the Azure Data Mesh as the core analytics toolset.

6. Governance, Controls & Ways of Working

• Ensure all analytical outputs adhere to Shell's data quality standards, credit risk policy, and regulatory requirements (including but not restricted to IFRS 9).

• Maintain clear documentation of analytical methodologies, data lineage, model assumptions, and solution architecture.

• Embed Agile/sprint working practices within the India team.

• Support audit, model validation, and compliance activities related to credit risk data and analytics as required.

• Proven track record of managing and developing analytical teams, including performance management, coaching, and workload prioritization.

• Significant experience in data, analytics, or credit risk function within financial services, energy trading, or a comparable environment.

• Hands-on proficiency with modern data platforms.

• Strong Python skills for data analysis and automation, with working knowledge of machine learning frameworks (e.g. scikit-learn, XGBoost, statsmodels) and data science libraries (pandas, numpy, scipy).

• Practical experience applying AI/ML techniques in an analytical or risk context – including predictive modelling, classification, anomaly detection, or NLP.

• Familiarity with LLM-assisted analytics tools and AI productivity tooling.

• Experience with GitHub or equivalent version control tools and development best practices for analytics code (testing, documentation, peer review).

• Excellent communication and stakeholder management skills, with the ability to translate complex technical and model outputs into clear business narratives for senior audiences.

• Demonstrated ability to manage competing priorities in a fast-paced, globally distributed organization.

• Strong understanding of credit risk concepts (counterparty credit risk, CVA, ECL, exposure measurement) and their data and reporting implications.

• Experience working within an energy trading, commodities, or ETRM (Energy Trading & Risk Management) environment.

• Familiarity with IFRS 9, Basel III, or other credit risk regulatory frameworks.

• Exposure to Azure Data Mesh architecture, data product thinking, or MLOps practices.

• Experience with data engineering and data transformation.

• Knowledge of Apache Airflow, Azure Data Factory, or equivalent orchestration tools for pipeline automation.

• Familiarity with Agile delivery methodologies (Scrum, Kanban) and sprint-based working.

• Experience working in a global, matrixed organization with reporting lines across geographies.

We are looking for a leader who combines strong technical credibility with the ability to inspire and develop their team. The successful candidate will demonstrate:

• Inclusive leadership – ability to foster a psychologically safe, collaborative team environment across cultures and time zones.

• Delivery focus – a bias for action, with a track record of holding themselves and others accountable to commitments.

• Curiosity and learning mindset – staying current with evolving data and analytics technologies and embedding this into team practice.

• Resilience and adaptability – able to navigate ambiguity in a major transformation program while keeping the team grounded and productive.

• Integrity – upholding Shell's values and ethical standards in all aspects of people management and analytical work.

An innovative place to work

There’s never been a more exciting time to work at Shell. Join us and you’ll be adding your talent and imagination to a business with the ambition to shape the future – whether by investing in oil, gas and renewable energy to meet demand, exploring new ways to store energy, or developing technology that helps the world to use energy more efficiently, everyone at Shell does their part.

The Market Risk Operations department is a specialized unit in Finance Operations that operates in the Chennai, Bangalore and Manila centers.  The team has front line responsibility for risk measurement, deal valuation, exposure reporting and price validation. We are interested in attracting people to our business who share our core working values of honesty, integrity and respect for others. We also want those who can build on our business vision, are prepared to accept accountability and can achieve great results through teamwork.

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