Data Scientist

About us 

Lantern isn’t your typical fintech start-up. It’s a pioneering Private Equity data intelligence platform with a bold mission to unleash the power of private markets data as a force for good. With our innovative SaaS platform and a passionate team, we’re taking on the challenge of disrupting the private markets industry, delivering powerful insights to GPs, LPs and Administrators.

Role Overview

You will play a key role in transforming complex private markets data into intelligent, scalable products. Working at the intersection of data engineering, AI, and product, you will design and deploy machine learning systems that power our data pipelines and deliver meaningful insights to our customers.

You’ll collaborate closely with engineering, product, and domain experts to build robust data extraction capabilities, integrate AI models into production workflows, and develop analytics tools that create measurable impact across customer experience, operational efficiency, and business performance.

This is a hands-on role where experimentation, rapid iteration, and production-grade implementation go hand in hand. We’re looking for someone who combines strong technical depth with a product mindset — someone who enjoys solving real user problems, shipping practical solutions, and continuously improving the quality and reliability of ML systems in a live SaaS environment.

Key Responsibilities:

• Support and build product-level scalable data extraction capabilities, including OCR- and NLP-driven pipelines for private markets documents and reporting data.
• Apply LLMs (via APIs) to real-world workflows, including handling variability in responses, prompt iteration, and system-level design.
• Develop lightweight production libraries for Data and Software Engineering teams to use.
• Develop analytics models and tools that leverage Lantern’s pipelines to deliver actionable insights into:
  • operational efficiency
  • product usage and performance
  • business performance metrics
• Collaborate cross-functionally with product, engineering, QA, and data teams to ensure models and pipelines meet real user needs.
• Build lightweight front-end tools for internal tools (e.g., Streamlit or equivalent) to quickly deliver value and enable rapid iteration.
• Implement and maintain strong testing practices (unit tests + end-to-end tests) for ML/AI systems during development and long-term support.
• Contribute to technical best practices across the DS project cycle, including monitoring, error analysis, documentation, and continuous improvement.
  

Requirements:

Must-Haves:

• Strong Python skills for data workflows and applied ML.
• A strong product-focused mindset, with a track record of solving real user problems.
• 2+ years of professional experience in data science or applied ML.
• Demonstrable experience delivering AI/ML projects using NLP and OCR.
• Owning the end-to-end DS cycle
• Experience building simple front-ends that deliver user value quickly.
• Strong approach to engineering quality, including:
  • Unit tests
  • End-to-end tests
  • Long-term support and reliability
    
• Experience with our technical stack: Python (NumPy, Pandas), SQL, OpenAI/AzureOpenAI, LangChain, Streamlit, Databricks, CI/CD, Docker.

Nice to Have

• Strong understanding of financial data structures (transactions, balances, reconciliations).
• Experience with private equity data and/or fund accounting.
• Experience with dbt and analytics engineering best practices.
• Familiarity with Snowflake performance and modelling patterns.
• Familiarity with cloud data services, preferably Azure.
• Azure, Postgres, Streamlit or equivalents
• Comfort working in small teams with fast iteration cycles

What We Value

• Humble – aware of your strengths and open about your learning areas.
• Growth-minded – committed to improving your skills and raising the bar as a team.
• Detail-oriented – accuracy matters, especially with financial data.

Why Join Us?

• Impactful Work: Play a crucial role in ensuring data integrity for critical private equity operations and reporting.
• Professional Growth: Opportunities to learn and grow in the private equity data space, with access to cutting-edge tools and technologies.
• Collaborative Environment: Work alongside a diverse team of data engineers, QA, product managers, and software engineers, contributing to a high-impact project.