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Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates extensive experience in fine-tuning transformers and LLMs for entity extraction and classification, with strong proficiency in Python and applied NLP/ML techniques. Capable of designing evaluation metrics and shipping models to production while effectively communicating results.
Highest-signal resume keywords
Fine-Tuning Transformer ModelsPython ProgrammingApplied NLP/ML ExperienceModel Evaluation DesignExperience with Learning-to-Rank
Tech Stack
Tools & technologiesAbout the role
Key responsibilities & impact- Fine-tune transformers and LLMs using LoRA/QLoRA for entity extraction, classification and reranking
- Extract entities from job descriptions and resumes
- Build prediction models, knowledge graphs and traversal models
- Build retrieval, clustering and learning-to-rank pipelines over large taxonomies
- Design evaluations and investigate model errors
- Ship models to production
Requirements
What you’ll need- 7+ years of experience stated for the role
- Hands-on applied NLP or ML experience: 1–3 years for Data Scientist I, 3–5 years for Data Scientist II
- Strong Python, PyTorch, Hugging Face Transformers, sentence-transformers, pandas and scikit-learn
- Experience fine-tuning transformer models on own data, including encoder models for NER or classification and decoder LLMs with PEFT (LoRA/QLoRA)
- Solid grasp of embeddings, attention, contrastive training, and retrieval with bi-encoders and cross-encoders
- Working knowledge of gradient boosting, logistic regression, clustering, and dimensionality reduction
- Comfort with statistics and evaluation design, including sampling, significance, calibration and metric choice
- SQL and ability to explore data at the scale of millions of records
- Clear written communication of experiments and results
- Experience with learning-to-rank, knowledge graphs or large taxonomies in a real product
- Experience with experiment tracking, model versioning, Docker, and GPU training and serving on AWS
- Nice to have: Neo4j, Cypher, pgvector, graph ML, node embeddings, link prediction, span-based or zero-shot NER, entity linking, LLM distillation, vLLM, quantisation, ONNX, AWS Bedrock, SageMaker, structured-output prompting, LangGraph, n8n, ontology or taxonomy design, HR tech/talent/skills data, open-source contributions, Kaggle results or NLP/IR publications
Benefits
Comp & perks- Competitive compensation
- Group medical cover for self, spouse, children and parents
- Hybrid work and flexible policies
- Retirement benefits
- A self-driven career progression framework
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Entity ExtractionClassificationPrediction ModelsKnowledge GraphsRetrieval PipelinesClusteringLearning-to-RankGradient BoostingLogistic RegressionDimensionality Reduction
Soft Skills
Clear Written Communication
Tools & Technologies
PyTorchHugging Face TransformersSentence-TransformersPandasScikit-LearnSQLDockerAWSNeo4jONNX
Industry Keywords
NLPMLHR TechEntity LinkingTaxonomy DesignOpen-Source ContributionsKaggle ResultsNLP Publications