SKILLS AND QUALIFICATIONS:

This is an opportunity for a candidate with:

• 3–4 years of professional experience in data science, AI systems, or related fields
• Experience building and deploying machine learning models and applications
• Strong proficiency in Python; familiarity with R and other programming or statistical languages is a plus
• Experience with prompting LLMs and building agentic AI systems (e.g. Langchain, Langgraph)
• Knowledge of NLP techniques such as entity extraction and similarity search; experience with knowledge graphs is a big plus.
• Experience integrating human feedback into AI workflows (e.g. using LabelStudio)
• Hands-on experience with NoSQL databases, particularly MongoDB
• Experience ingesting unstructured data from websites using scraping tools (e.g. BeautifulSoup, Selenium)
• Working knowledge of AWS Cloud services (EC2, S3)
• Comfortable working in Unix environments and managing Python packages.
• Degree in Computer Science, Data Science, Engineering, or a related field; Master’s degree is a plus

This is a full-time, on-going position.
Remuneration is competitive and includes a bonus for meeting performance objectives.
Attractive salary and bonus, healthcare and pension plan coverage. Flexible work is available, but with an expectation of a minimum three days in the office. The salary will be determined according to EIPA’s pay scale, the candidate’s qualifications and experience.
To apply for this offer, please send a CV and a cover letter (pdf format)

• The successful applicant will be responsible for the development of model pipeline tools to convert unstructured financial and textual data into structured data formats.
• Support the development and refinement of tools for company identification, transaction and source matching, as well as company data and ownership mapping using knowledge graphs.
• Assist in improving algorithmic models for private equity transaction matching and support the retrieval of financial accounts by integrating with available APIs or automating the acquisition of company reports.
• Maintain and enhance existing machine learning tools, collaborating closely with analysts to ensure smooth integration into the operational workflow.
• Liaise with the production team to implement and maintain the developed models.
• Work closely with the Head of Data to develop other data products as necessary.