Senior Data Scientist

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)