Job Description

Data Scientist – ML Consulting (Remote)

Must be a US Citizen or Green Card holder to be eligible for this role.

We're looking for a senior Data Scientist to join our customer-facing consulting team. This is a hands-on technical role where you'll lead end-to-end ML implementations for clients — from architecture through production deployment — with a strong emphasis on GenAI, NLP, and MLOps.

What You'll Do

• Lead client engagements — serve as the primary technical consultant, translating complex business problems into production-grade ML solutions and communicating clearly with both technical and non-technical stakeholders.
• Build and maintain ML pipelines — design robust CI/CD-enabled pipelines with best-in-class MLOps practices: model versioning, testing, monitoring, and automated deployment.
• Deploy GenAI and NLP applications — implement and optimize LLM-based solutions, including RAG architectures, in live client environments.
• Manage solution infrastructure — work with Docker, pipeline orchestrators, and database systems to support scalable ML deployments.
• Leverage distributed computing — apply frameworks like Apache Spark for high-performance, large-scale data processing.
• Support practice growth — contribute to knowledge sharing, internal technical initiatives, and documentation via partner platforms.

Required Qualifications

• 4+ years of hands-on experience developing, deploying, and maintaining ML models in production environments (productionization experience is a must)
• 3+ years in a customer-facing consulting or solutions architect role with a focus on technical delivery
• Strong MLOps experience: model lifecycle management, versioning, monitoring, and automated deployment
• Proficiency with containerization (Docker) and data pipeline orchestration
• Proven experience deploying Generative AI and NLP solutions for client applications
• Excellent written and verbal communication skills

Preferred Qualifications

• Hands-on experience with Databricks MLOps Stacks or similar modern ML platform stacks
• Familiarity with scalable ML tooling and large-scale data processing frameworks
• Active engagement with emerging ML fields — LLMs, GenAI application architectures, etc.