Data Scientist- Business Risk

HSBC; Bengaluru; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

HSBC is seeking an experienced Data Scientist-Business Risk to lead fraud analytics for Merchant Card Acquiring. You will partner with business, operations, technology and risk teams to drive data-driven decisions, design fraud typologies, and deploy scalable analytics across markets.

The role requires 6+ years in banking/financial services, strong Python/SQL skills, and cloud experience (GCP/AWS/Azure). You will communicate complex findings to executives and support priority fraud themes with

Qualifications

• Degree-level qualification in Data Science, Computer Science, Statistics, Mathematics, Finance or another quantitative discipline.
• 6+ years of relevant experience in banking/financial services with advanced analytics like regression, predictive modelling, data mining and machine learning.
• Proficiency in Python and SQL; experience with Alteryx; hands-on with SAS, Spark and cloud platforms (GCP/AWS/Azure).
• Strong data mining/transformation skills across structured and unstructured data; familiarity with Agile and cross-functional delivery.
• Experience with data visualisation tools (e.g., Qlik Sense) is a plus.
• Knowledge of financial crime/fraud context is beneficial.

Responsibilities

• Act as a fraud analytics SME and trusted adviser for CIB Business Risk, supporting Fraud Risk Management within Merchant Card Acquiring across regions.
• Partner with business, operations, technology, product, compliance and risk teams to drive data‑led decisions for reducing fraud losses while protecting merchant/customer experience.
• Maintain understanding of merchant acquiring lifecycle and key fraud risks across onboarding, underwriting, processing, settlement, and disputes/chargebacks.
• Develop analytics strategies aligned to business priorities, improving detection, reducing false positives, and building scalable analytics assets for deployment across markets.
• Communicate complex findings in executive-ready formats to enable timely governance and decisions.
• Design and implement fraud typologies across onboarding, underwriting, and early-life monitoring; monitor for anomalies and spikes.
• Develop rules/scorecards for near-real-time monitoring; use network/graph analytics to identify linked merchants and clusters.
• Support priority fraud themes such as CNP fraud, refund/chargeback abuse, and compromised merchants.
• Deliver robust ETL/data pipelines across transaction feeds, master data, onboarding data, and disputes; leverage cloud platforms (GCP/AWS/Azure) for scalable delivery.
• Engage and collaborate with stakeholders across the business.

Skills

Python

SQL

Machine learning

Data mining

Statistical modelling

Education

Degree-level qualification in Data Science, CS, Statistics

Tools

Alteryx

SAS

Spark

GCP

AWS

Azure

Hadoop

Job description

Data Scientist-Business Risk

HSBC is one of the largest banking and financial services organisations in the world. We are seeking an experienced professional to join our team in this role.

Responsibilities

• Act as a fraud analytics SME and trusted advisor for CIB Business Risk, supporting Fraud Risk Management within the Merchant Card Acquiring business across one or more regions.
• Partner with Acquiring business teams, Operations, Technology, Product, Compliance and Financial Crime Risk to drive data‑led decisions that reduce fraud losses while protecting merchant and customer experience.
• Maintain a strong understanding of the merchant acquiring lifecycle (onboarding, underwriting, transaction processing, settlement, and disputes/chargebacks) and the key fraud risks and control points across each stage.
• Develop and support analytics strategies aligned to business priorities, including improved detection effectiveness, reduced false positives and stronger control performance, creating reusable and scalable analytical assets (typologies, features, rules, model components and monitoring packs) that can be deployed consistently across markets and merchant segments.
• Communicate complex analytical findings in a clear, executive‑ready format, enabling timely decisions and effective governance while consistently upholding HSBC Core Values.
• Design and implement fraud typologies across the merchant lifecycle, covering onboarding and underwriting (e.g., suspicious applications, high‑risk profiles) and early‑life monitoring (e.g., rapid spikes in activity, abnormal refunds/voids).
• Portfolio monitoring via behavioural drift, emerging patterns and peer outliers, and build operationalised detection approaches such as anomaly detection for volume, value, velocity, geography, refund ratios and chargeback rates.
• Develop rules and scorecards to support near‑real‑time monitoring and triage, and employ network/graph analytics to identify linked merchants and suspicious relationship clusters. Segment and benchmark peers by MCC, channel (e‑commerce vs card‑present), region and merchant size.
• Support priority fraud themes including CNP fraud patterns, refund/chargeback abuse and indicators of compromised merchants, ensuring solutions are practical for operational adoption.
• Deliver robust ETL and data pipeline capabilities across acquiring transaction feeds, merchant master data, onboarding data and disputes/chargebacks, ensuring strong data quality and integrity. Leverage cloud analytics platforms such as GCP, AWS (including Redshift) and/or Azure to support scalable delivery and deployment.
• Engage and collaborate with stakeholders across the business.

Qualifications

• Degree‑level qualification (or equivalent relevant experience) in Data Science, Computer Science, Statistics, Mathematics, Finance or another quantitative discipline.
• 6+ years of relevant experience, ideally within banking or financial services, with strong grounding in advanced analytical methods such as regression, predictive modelling, data mining and machine learning, and a structured and creative problem‑solving approach.
• Proficiency in Python, SQL (or similar analytical programming languages), Alteryx and experience with SAS, Spark and Google Cloud Platform. Hands‑on experience with cloud analytics platforms (GCP, Azure, AWS) and big data technologies (Hadoop, Spark).
• Strong technical skills in data mining and transformation, ability to work across structured and unstructured data and varied data models.
• Familiarity with Agile methodologies and collaborative delivery in cross‑functional teams.
• Experience with data visualisation tools such as Qlik Sense is a strong advantage.
• High technical aptitude, intellectual curiosity, strong communication and interpersonal skills and clear ownership and accountability.
• Experience in a large corporate or institutional acquiring environment; supporting the build or scaling of a new fraud operations function or product.
• Knowledge of financial crime and fraud is beneficial but not essential. Understanding of banking use cases translating into data science solutions is a plus and familiarity with scheme monitoring programmes (Visa Fraud Monitoring Program, Mastercard Excessive Fraud Merchant thresholds).
• Sound knowledge of the Risk Management Framework (expertise not required).

HSBC is an equal opportunity employer committed to building a culture where all employees are valued, respected and opinions count. We take pride in providing a workplace that fosters continuous professional development, flexible working and opportunities to grow within an inclusive and diverse environment. We encourage applications from all suitably qualified persons irrespective of gender, sexual orientation, ethnicity, religion, social status, disability or veteran status.

Personal data held by the Bank relating to employment applications will be used in accordance with our Privacy Statement, which is available on our website.

HSBC Careers page: www.hsbc.com/careers