Senior data scientist

Senior Data Scientist - NLPPosition OverviewSenior-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 QualificationsExperience- 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 infrastructureTechnical SkillsSQL- Advanced query optimization and performance tuning- Complex joins, window functions, CTEs- Experience with Snowflake, Big Query, or Redshift- Database performance analysis and indexing strategiesNLP Technology Stack- Transformer architectures- RAG pipeline implementation- Lang Chain, Llama Index, or similar frameworks- Vector databases: Pinecone, Weaviate, Chroma, FAISS- Model fine-tuning: Lo RA, QLo RA- Embedding models and semantic search- Prompt engineering techniquesProgramming & ML Frameworks- Python (advanced level, production-grade code)- Py Torch or Tensor Flow- Hugging Face Transformers- scikit-learn, XGBoost, Light GBMInfrastructure & Dev Ops- Linux system administration- Bare metal server management- GPU cluster setup and configuration- CUDA/cu DNN installation and driver management- Multi-GPU distributed training setup- Docker and Kubernetes- CI/CD pipelines for ML workflowsProduction Deployment- Model serving: Tensor Flow Serving, Torch Serve, Fast API, Bento ML- MLOps: MLflow, Weights & Biases, Kubeflow- Model monitoring and A/B testing- Latency optimization and inference scalingCloud & Data Engineering- AWS, GCP, or Azure- Apache Spark, Airflow/Prefect- Understanding of on-premise and cloud hybrid architecturesKey ResponsibilitiesTechnical 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 techniquesLeadership & 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 solutionsTeam 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 practicesRequired 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 skillsEvaluation 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 problemPreferred Qualifications- Experience with pre-training multi-modal models (vision-language, audio-text, etc.)- Hands-on experience with large-scale distributed training frameworks (Deep Speed, FSDP, Megatron-LM)- Contributions to open source ML projects- Technical blog or active Git Hub portfolio- Experience with model quantization and efficient inference- Publications or conference presentations- Knowledge of multi-modal architectures (CLIP, Flamingo, GPT-4 V style models)