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)