Senior Data Scientist

2 days ago

Hyderabad, Telangana, India QYLIS Full time

We are seeking a Senior Data Scientist to lead data-driven decision-making across LLM and SLM initiatives. This role sits at the intersection of data engineering, ML engineering, and applied AI, and is responsible for model selection, data quality, training strategies, and human-in-the-loop learning systems.

The role emphasizes judgment and depthdeciding what to train, how to train, and how to measure successrather than owning infrastructure or pure research.

Key Responsibilities

Model Strategy & Selection-

  • Work with ML Engineers and Architects to evaluate and select LLMs and SLMs based on performance, cost, latency, privacy, and deployment constraints
  • Define modeling approaches (prompting, RAG, fine-tuning, distillation) aligned to business use cases
  • Establish benchmarking frameworks to compare open-source and proprietary models

Data Preparation & Training Readiness

  • Partner with Data Engineering teams to design data pipelines for ingestion, cleaning, normalization, deduplication, and document chunking
  • Define dataset standards, schemas, and quality metrics for pre-training and fine-tuning
  • Ensure dataset lineage, versioning, and reproducibility across experiments

RLHF & Human-in-the-Loop Learning

  • Design and lead RLHF / DPO pipelines, including preference collection and reward modeling
  • Define annotation guidelines, scoring rubrics, and gold datasets
  • Measure and improve data quality using inter-annotator agreement and bias metrics
  • Continuously refine feedback loops to improve model alignment and reliability

Evaluation & Governance

  • Define model evaluation frameworks covering relevance, hallucination, bias, robustness, and safety
  • Support responsible AI practices and alignment with enterprise governance requirements
  • Collaborate with product and business teams to translate outcomes into measurable impact

Technical Leadership

  • Provide technical guidance and mentorship to data scientists and analysts
  • Review experiments, data quality, and evaluation results
  • Act as a bridge between data, engineering, and product teams

Required Skills & Experience

  • Strong foundation in Data Science, Statistics, and Applied Machine Learning
  • Hands-on experience with LLMs / SLMs, including fine-tuning and evaluation
  • Expertise in Python, SQL, and large-scale text data processing
  • Practical experience with RLHF, DPO, preference learning, or reward modeling
  • Familiarity with PyTorch, Hugging Face, MLflow/W&B, and annotation tools
  • Proven ability to operate in cross-functional, fast-moving environments

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