Senior Data Scientist - NLP

Senior Data Scientist - NLP

Position Overview

Senior-level data scientist role focused on building and deploying production NLP systems on bare metal infrastructure. This position requires a research-oriented mindset with the ability to build first-in-class products by translating cutting-edge research into innovative production solutions.

Required Qualifications

Experience

• Minimum 5 years in data science/ML engineering roles
• Minimum 3 years tenure in most recent organization in a relevant data science/ML role
• Proven track record of deploying ML models to production
• Experience managing bare metal server infrastructure

Technical Skills

SQL

• Advanced query optimization and performance tuning
• Complex joins, window functions, CTEs
• Experience with Snowflake, BigQuery, or Redshift
• Database performance analysis and indexing strategies

NLP Technology Stack

• Transformer architectures
• RAG pipeline implementation
• LangChain, LlamaIndex, or similar frameworks
• Vector databases: Pinecone, Weaviate, Chroma, FAISS
• Model fine-tuning: LoRA, QLoRA
• Embedding models and semantic search
• Prompt engineering techniques

Programming & ML Frameworks

• Python (advanced level, production-grade code)
• PyTorch or TensorFlow
• HuggingFace Transformers
• scikit-learn, XGBoost, LightGBM

Infrastructure & DevOps

• Linux system administration
• Bare metal server management
• GPU cluster setup and configuration
• CUDA/cuDNN installation and driver management
• Multi-GPU distributed training setup
• Docker and Kubernetes
• CI/CD pipelines for ML workflows

Production Deployment

• Model serving: TensorFlow Serving, TorchServe, FastAPI, BentoML
• MLOps: MLflow, Weights & Biases, Kubeflow
• Model monitoring and A/B testing
• Latency optimization and inference scaling

Cloud & Data Engineering

• AWS, GCP, or Azure
• Apache Spark, Airflow/Prefect
• Understanding of on-premise and cloud hybrid architectures

Key Responsibilities

Technical Execution

• Design and implement production NLP solutions using state-of-the-art language models
• Build and optimize complex SQL data pipelines processing millions of records
• Deploy ML models on bare metal GPU infrastructure
• Configure and maintain GPU clusters for training and inference
• Implement MLOps practices: versioning, monitoring, automated retraining
• Optimize model inference for latency and throughput
• Troubleshoot CUDA, driver, and hardware-level issues
• Set up distributed training across physical servers
• Research and prototype emerging ML techniques

Leadership & Strategy

• Lead end-to-end ML projects from problem definition to production deployment
• Drive innovation by researching and implementing first-in-class product features
• Coordinate cross-functional teams including data engineers, domain experts, and full-stack developers to deliver integrated solutions
• Define technical architecture and design decisions for ML systems
• Drive adoption of ML best practices and engineering standards across teams
• Collaborate with product and engineering leadership on ML roadmap and priorities
• Present technical findings and recommendations to executive stakeholders
• Own critical ML infrastructure decisions and vendor evaluations
• Champion innovation by evaluating and integrating cutting-edge ML research
• Lead cross-functional initiatives between data science, engineering, and product teams
• Facilitate effective collaboration between technical and non-technical stakeholders
• Translate latest research papers into production-ready solutions

Team Development

• Mentor and coach junior data scientists and ML engineers
• Conduct code reviews and provide technical guidance
• Develop training materials and knowledge-sharing sessions
• Participate in hiring and building the data science team
• Establish coding standards and documentation practices

Required Competencies

• Research-oriented mindset with ability to innovate and build first-in-class products
• Ability to work independently with minimal supervision and drive projects autonomously
• Strong analytical and quantitative aptitude
• Excellent problem-solving and logical reasoning skills
• Proven ability to collaborate with cross-functional teams (data engineers, domain experts, full-stack developers)
• Strong communication skills to translate technical concepts for non-technical stakeholders
• Willingness to explore uncharted territory and experiment with novel approaches
• Self-motivated with strong ownership mentality
• Strong understanding of hardware constraints and optimization
• Ability to work independently with bare metal infrastructure
• Experience with both cloud and on-premise deployments
• Proven ability to take projects from research to production
• Track record of staying current with ML research and innovations
• Strong debugging and troubleshooting skills

Evaluation Process

• SQL optimization and Python coding assessment
• ML system design interview
• Technical deep-dive on NLP and production ML
• Take-home project: end-to-end ML problem

Preferred Qualifications

• Experience with pre-training multi-modal models (vision-language, audio-text, etc.)
• Hands-on experience with large-scale distributed training frameworks (DeepSpeed, FSDP, Megatron-LM)
• Contributions to open source ML projects
• Technical blog or active GitHub portfolio
• Experience with model quantization and efficient inference
• Publications or conference presentations
• Knowledge of multi-modal architectures (CLIP, Flamingo, GPT-4V style models)