Data Scientist Transaction monitoring (ID
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Experience Required:
Posted on:
5 hours ago
Vacancies:
1 Vacancy
Job Summary
- Build and optimize automated data pipelines and workflows for transaction monitoring across large-scale datasets
- Perform end-to-end data analysis: from intake and coding to visualization and stakeholder presentation
- Design develop and deploy dashboards (front-end to back-end) to support rule tuning development and monitoring insights
- Analyze and estimate the impact of parameter changes on model efficiency and detection performance
- Improve workflows to enable more efficient tuning across multiple parameter combinations
- Collaborate with cross-functional teams to bring dashboards and analytical solutions into production
- Contribute to detecting and preventing financial crime by identifying patterns and anomalies in transaction data
- 5 years of experience in data analysis using Python
- Strong experience with PySpark Databricks and Power BI
- Experience building end-to-end data solutions including version control pipelines and testing frameworks
- Ability to guide junior and mid-level team members and share technical expertise
- Translate business problems into data-driven solutions and uncover the real question behind requirements
- Work in a structured precise and proactive manner with strong communication skills
- Present insights and recommendations clearly and convincingly to stakeholders
- Balance technical depth with business understanding to deliver impactful outcomes
- Think strategically identify future opportunities and contribute to continuous improvement
- Opportunity to work on large-scale high-impact data problems involving millions of users
- A role focused on meaningful work in detecting and preventing financial crime
- Hybrid working model with fixed collaboration days
- Long-term engagement with potential for extension
- A collaborative environment where innovation ownership and continuous improvement are encouraged
As a Data Scientist (Transaction Monitoring) you will: Build and optimize automated data pipelines and workflows for transaction monitoring across large-scale datasets Perform end-to-end data analysis: from intake and coding to visualization and stakeholder presentation Design develop and deploy dashboards (front-end to back-end) to support rule tuning development and monitoring insights Analyze and estimate the impact of parameter changes on model efficiency and detection performance Improve workflows to enable more efficient tuning across multiple parameter combinations Collaborate with cross-functional teams to bring dashboards and analytical solutions into production Contribute to detecting and preventing financial crime by identifying patterns and anomalies in transaction data What You Bring to the Table: 5 years of experience in data analysis using Python Strong experience with PySpark Databricks and Power BI Experience building end-to-end data solutions including version control pipelines and testing frameworks Ability to guide junior and mid-level team members and share technical expertise You should possess the ability to: Translate business problems into data-driven solutions and uncover the real question behind requirements Work in a structured precise and proactive manner with strong communication skills Present insights and recommendations clearly and convincingly to stakeholders Balance technical depth with business understanding to deliver impactful outcomes Think strategically identify future opportunities and contribute to continuous improvement What we bring to the table: Opportunity to work on large-scale high-impact data problems involving millions of users A role focused on meaningful work in detecting and preventing financial crime Hybrid working model with fixed collaboration days Long-term engagement with potential for extension A collaborative environment where innovation ownership and continuous improvement are encouraged Lets Connect Want to discuss this opportunity in more detail Feel free to reach out. Recruiter: Aswin Dhanvandhar Phone:; Extn :141 Email: LinkedIn:
