Fraud Data Scientist - 24010313

at  MoneyGram

MoneyGram, a leading Fintech, is seeking a highly motivated Data Scientist to join our dynamic team. This entry-level position is ideal for a new graduate who is passionate about leveraging data science and machine learning to drive innovation and efficiency. The ideal candidate will have a strong foundation in data science principles, experience with machine learning models, and a keen interest in AI technologies.

Responsibilities:

• Data Enrichment: Enhance the MoneyGram ecosystem’s raw data to create actionable insights for model training and application. Knowledge of SQL/BigQuery/Splunk necessary.
• Distributed Computing: Use frameworks like Apache Spark or Databricks to perform real-time and batch data processing at scale.
• Analytical Support: Work with both technical and non-technical stakeholders to fulfil ad-hoc analytics requests, employing strong problem-solving skills to identify and mitigate risks.
• Apply strong problem-solving skills to identify risk factors and propose mitigation strategies effectively.
• Mentorship and Leadership: Provide guidance and mentorship to junior analysts and other team members, fostering a culture of learning and growth within the team.
• Process Optimization: Analyze and improve existing data workflows, proposing and implementing new processes to increase data collection and processing efficiency and accuracy.
• Data Visualization and Reporting: Develop comprehensive dashboards and reports to visualize key metrics and trends for stakeholders, facilitating data-driven decisions.
• Advanced Data Analytics: Perform complex data analysis involving predictive modelling, regression analysis, and statistical testing to inform business decisions and strategy.
• Model Development & Optimization: Create, tune, and optimize deep learning models to ensure high accuracy and efficiency. Continuously monitor and enhance the performance of machine learning models.
• Research & Innovation: Stay abreast of the latest developments in AI, machine learning, and cloud technologies. Contribute to internal knowledge-sharing and innovation initiatives.

Qualifications
-
- Master’s degree in Computer Science, Data Science, Statistics, or a related field.
- Solid understanding of data structures and software development principles.
- Experience with Python, R, or similar programming languages.
- Strong problem-solving skills and the ability to work in a fast-paced, team-oriented environment.
- Data Engineering Skills: Competence in data engineering, including experience with big-data technologies, data modelling, and integration from diverse sources.
- Experience with distributed systems concepts such as sharding, replication, and caching.
- Performance Optimization: Ability to identify and resolve performance bottlenecks in data-intensive applications.
- Complex Algorithm Understanding: Deep understanding of data mining and data insights.
- Knowledge of multi-threading, asynchronous I/O, and design patterns.
- Experience with predictive analytics, deep learning models and recommendation systems is a plus.
- Visualization Expertise: Proficiency in creating insightful visualizations and reports using tools like Tableau, PowerBI, Splunk or Looker.
- Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
- Leadership Experience: Proven ability to lead projects and small teams with minimal supervision.
Primary Location: United States of America-Texas-Dallas
Work Locations: VIRTUAL EMPLOYEES
Job: Data Operations
Organization: Global Operations
: Full-time
Job Posting: Apr 18, 2024, 11:51:21 A

• Data Enrichment: Enhance the MoneyGram ecosystem’s raw data to create actionable insights for model training and application. Knowledge of SQL/BigQuery/Splunk necessary.
• Distributed Computing: Use frameworks like Apache Spark or Databricks to perform real-time and batch data processing at scale.
• Analytical Support: Work with both technical and non-technical stakeholders to fulfil ad-hoc analytics requests, employing strong problem-solving skills to identify and mitigate risks.
• Apply strong problem-solving skills to identify risk factors and propose mitigation strategies effectively.
• Mentorship and Leadership: Provide guidance and mentorship to junior analysts and other team members, fostering a culture of learning and growth within the team.
• Process Optimization: Analyze and improve existing data workflows, proposing and implementing new processes to increase data collection and processing efficiency and accuracy.
• Data Visualization and Reporting: Develop comprehensive dashboards and reports to visualize key metrics and trends for stakeholders, facilitating data-driven decisions.
• Advanced Data Analytics: Perform complex data analysis involving predictive modelling, regression analysis, and statistical testing to inform business decisions and strategy.
• Model Development & Optimization: Create, tune, and optimize deep learning models to ensure high accuracy and efficiency. Continuously monitor and enhance the performance of machine learning models.
• Research & Innovation: Stay abreast of the latest developments in AI, machine learning, and cloud technologies. Contribute to internal knowledge-sharing and innovation initiatives