Data Scientist
Requirements:
- Proven experience as a Data Scientist with a focus on AI and machine learning applications.
- Expertise in developing models using Language Models (LLM) for natural language processing tasks.
- Strong background in data analytics, statistical analysis, and machine learning concepts.
- Proficient in writing complex SQL queries for data extraction and analysis.
- Excellent problem-solving skills and the ability to think creatively to overcome challenges.
- Strong communication skills to effectively convey technical concepts to both technical and non-technical stakeholders.
- Ability to work collaboratively in a team environment and take complete ownership of the product.
Key responsibilities:
Payslip Data Extraction:
- Design, develop, and implement advanced models using cutting-edge AI technologies, with a primary focus on Language Models (LLM), to extract relevant information from payslips.
- Collaborate with cross-functional teams to understand product requirements, define objectives, and deliver robust solutions for efficient payslip data extraction.
Fraud Detection:
- Build and deploy models for detecting fraudulent patterns and anomalies within payslip data.
- Utilize machine learning techniques to enhance fraud detection algorithms, ensuring accuracy and adaptability to evolving patterns.
AI Technology Integration:
- Stay updated on the latest advancements in AI and machine learning and apply state-of-the-art techniques, such as LLM, to continually improve model's outcomes.
- Work closely with the technology team to integrate AI models into the existing infrastructure, ensuring seamless functionality.
Data Analytics:
- Conduct thorough data analysis to identify trends, patterns, and insights that contribute to the refinement of payslip data extraction and fraud detection models.
- Provide actionable recommendations based on data insights to enhance overall performance.
SQL Expertise:
- Write and optimize SQL queries to efficiently retrieve, manipulate, and analyse large datasets relevant to payslip information and fraud detection
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