Description:
As a Solutions Architect, you will play a key role in designing, developing, and implementing advanced fraud detection and prevention solutions. You will work closely with cross-functional teams, including data scientists, engineers, and product managers, to ensure our solutions are robust, scalable, and effective. This role requires a deep understanding of machine learning algorithms, data science techniques, and a passion for solving complex problems.
Key Responsibilities:
- Design and develop fraud detection, prevention and AML solutions using advanced machine learning algorithms and statistical techniques.
- Collaborate with data scientists and engineers to build scalable and efficient data pipelines for handling large datasets.
- Analyze complex data sets to identify patterns, anomalies, and potential fraud activities.
- Implement data cleaning, transformation, and quality assurance processes to ensure the accuracy and reliability of data.
- Communicate complex concepts and findings to both technical and non-technical audiences, providing actionable insights.
- Serve as the primary technical point of contact for customers, utilizing tools such as Elasticsearch, Snowflake, AWS S3, AWS Lambda, and PostgreSQL.
Requirements:
- 4+ years of experience in data science or machine learning, or a related field, with a focus on fraud detection, prevention or anti-money laundering.
- In-depth knowledge of risk domains, such as AML, KYC/KYB, Fraud, or Underwriting.
- Hands-on experience with AWS technologies, including Lambda, CloudWatch, and Cognito.
- Proficient in Python scripting.
- Proficiency in Python.
- Skilled in PostgreSQL
- Experience working with large datasets and handling data-related challenges such as data cleaning, data quality, and data transformation.
- Excellent analytical and problem-solving skills, with the ability to derive actionable insights from complex data.