Assistant Analytics Manager – Fraud & AML (Gurugram)

Job Description About The Company Founders today are building global companies from day one — but the systems that manage their money were built for a different era. Aspire exists to change that! We're building the financial operating system for global founders, bringing banking, software, and automation into a single platform so businesses can move faster across borders and stay focused on building. Aspire is built by people who think from first principles, care deeply about solving hard problems, and take real ownership of their work. Our team brings global experience from leading fintech and technology companies, and many of us are former founders and operators who understand what it takes to build thoughtfully, make trade-offs, and deliver at scale in a global environment. Backed by leading global investors including Y Combinator, Peak XV, and Lightspeed, Aspire has been trusted by more than 50,000 startups and growing businesses worldwide to manage their finances since 2018. Together with partners like J.P. Morgan, Visa, and Wise, we're building for the next generation of global companies. About The Team At Aspire, the Data Team is the engine that powers smart decisions across the company. We turn raw information into strategic insights, helping every team ,from Product and Operations to Finance , move faster and act smarter. We combine rigorous analytical thinking, deep business understanding, and robust technical skills to solve complex problems and uncover hidden opportunities.

Whether it's building scalable data infrastructure, designing intelligent dashboards, or running deep-dive analyses, we are trusted partners in driving impact. Within this team, our Fraud & AML Analytics function plays a critical role in protecting our customers and the integrity of Aspire's platform. We sit at the intersection of financial crime prevention, data science, and product , partnering closely with our Risk, Compliance, and Transaction Screening teams to build detection systems and analytical frameworks that scale with our business. About The Role We are searching for an Assistant Analytics Manager – Fraud & AML to join our dynamic team. In this role, you will be at the forefront of financial crime analytics, partnering closely with our Risk and Compliance leadership to detect fraud patterns, support AML investigations, and build data infrastructure that enables proactive financial crime prevention at scale. This is a high-visibility role that blends data science, BI engineering, and domain expertise in fraud/AML, ideal for someone who can move fluidly between writing detection models and building dashboards for compliance stakeholders. What You Will Be Doing

- Own Fraud & AML Metrics & Reporting: Design, validate, and maintain KPIs and metrics covering fraud rates, false positive rates, AML alert volumes, SAR filing rates, and detection model performance.
- Build Scalable Data Infrastructure and ML models: Build and maintain data pipelines ,dashboards and machine learning models that give Risk, Compliance, and Operations teams real-time visibility into fraud trends, transaction screening outcomes, ,typology patterns and predict alerts.
- Drive Detection Analytics: Develop and refine rule-based and ML-assisted detection models for transaction fraud, account takeover, and AML typology identification
- Support New Control Launches: Lead the analytical setup for new fraud/AML controls and product risk features, ensuring metrics, monitoring, and cost attribution are in place from day one.
- Enable Self-Serve Analytics for Investigations: Partner with Compliance and Operations teams to respond to ad-hoc investigation data needs and build self-serve tooling that accelerates case review workflows.
- Streamline with Automation: Automate recurring compliance and risk reporting processes (e.g., periodic review reporting, regulatory metrics) to free analyst capacity for higher-value work.
- Collaborate Cross-Functionally: Work closely with Product, Risk, Operations, and Finance teams to translate regulatory and business requirements into clear analytical solutions.
- Track and Optimise Costs: Support centralized cost tracking for fraud losses and AML programme operations and generate management reports for senior stakeholders.
- Mine Insights for Impact: Dive deep into transactional and behavioural data to identify emerging fraud typologies, money laundering patterns, and anomalies before they become material risks. We Would Love to Get to Know You If You Have the Following:
- Bachelor's degree in business, Finance, Economics, Computer Science, Statistics, or a related field.
- 4–8 years of relevant experience in Fraud Analytics, AML/Financial Crime, Risk Data Science, or a related field within financial services or fintech.
- Hands-on experience with AML or fraud detection systems, including transaction monitoring rules, model validation, or SAR/STR reporting workflows.
- A proactive mindset with a passion for diving deep into data to uncover anomalous patterns and drive risk-informed decisions.
- Strong organisational, planning, and communication skills to manage competing priorities across Risk, Compliance, and Product stakeholders.
- Proven ability to translate complex technical findings into clear, actionable insights for compliance and operations audiences.
- Ability to manage multiple workstreams in a fast-paced, regulated environment. Technical Skills

- SQL: Ability to write and optimise complex, multi-CTE analytical queries using window functions, pivots/unpivots, and large-scale transactional datasets — ideally applied to fraud/AML use cases.
- Python for Analytics & Data Science: Proficient in Python (Pandas, Num Py, Scikit-learn) for data transformation, anomaly detection, rule simulation, and scenario modelling.
- Data Modelling & Metrics Engineering: Experience designing analysis-ready data models and defining consistent risk metrics (e.g., fraud rate, alert-to-SAR conversion, detection precision/recall).
- BI & Dashboarding: Capable of building and maintaining production-grade dashboards (Databricks, Tableau, or similar) covering fraud monitoring, AML typology reporting, and compliance KPIs.
- Machine Learning for Financial Crime: Exposure to ML techniques applied to fraud/AML (e.g., anomaly detection, network analysis, supervised classification for transaction risk scoring) is a strong plus.
- Regulatory Familiarity: Familiarity with AML regulatory frameworks (e.g., MAS Notice 626, FATF recommendations) and/or fraud risk management standards is preferred but not essential. What We Offer

- Work from anywhere
- Uncapped flexible annual leave.
- Training subsidy for your professional growth.
- Wellness benefit.
- Team bonding budget to foster collaboration and sense of belonging.
- A culture that brings special talents together — learn more at our careers site and Linked In Life page. By submitting this application, I confirm that all the information given by me in this application for employment and any additional documents attached hereto are true to the best of my knowledge and that I have not wilfully suppressed any material fact. I confirm I have disclosed if applicable any previous employment with Aspire. I accept that if any of the information given by me in this application is in any way false or incorrect, my application may be rejected, any offer of employment may be withdrawn or my employment with Aspire may be terminated summarily or I may be dismissed.

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