Tartan - Data Scientist

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