Back
Known data firm, metro location, mid-level generalist title, and 2-5 years experience increase applicant competition.
Core ML and NLP skills transfer across industries, but document intelligence and finance domain expertise increases fit sensitivity.
Role requires hands-on ML/NLP production experience and specific frameworks, enforcing moderate candidate filtering.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Own end-to-end AI and ML solutions for extracting structured data from reports, news, and content using NLP, generative AI, and related techniques.
Define problem statements, develop models, handle production deployment, monitor, and continuously improve data science initiatives.
Collaborate cross-functionally with product, engineering, and domain experts, ensuring model quality and measurable business impact post-launch.
Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Economics, Engineering, or a related quantitative field.
Minimum 2+ years of experience in applied data science, machine learning, NLP, or information extraction.
Proficiency in Python, SQL, and ML frameworks such as PyTorch or TensorFlow; experience with NLP/LLM frameworks like Hugging Face or LangChain.
Work Experience Required: 2+ years in relevant applied data science roles.
Experienced in developing document intelligence and information extraction solutions using transformers, embeddings, RAG, LLMs, and prompt engineering.
Strong analytical skills with demonstrated ability to translate business needs into measurable ML problems and conduct experimental design and error analysis.
Comfortable working in hybrid office settings with cross-functional teams involving product managers, engineers, and domain specialists for scalable AI/ML deployments.