Data Scientist - INDIA at Vytwo

Prosper, Texas, United States -

Full Time

Start Date

Immediate

Expiry Date

19 Jun, 26

Salary

0.0

Posted On

21 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Databricks, PySpark, Azure Cloud Services, ML Algorithms, Statistical Methods, PyTorch, TensorFlow, Transformers, HuggingFace, Model Monitoring, Prompt Engineering, RAG, LLM Agentic Frameworks, Vector Databases, MLOps, Git

Industry

IT Services and IT Consulting

Description

Role: Data Scientist - INDIA Location: Hyderabad, INDIA *Consultants local to INDIA are eligible. Category: Data Science – Structured Data / Text Data (NLP & GenAI) About the Role We are seeking a highly skilled Data Scientist (3–7 years of experience) to join our team and work across two major data science domains: Structured Data (80–90%) – Predictive analytics, forecasting, cost estimation, likelihood modeling, and batch‑oriented machine learning pipelines. Text / Unstructured Data (NLP & GenAI) – Building low‑latency real‑time systems using deep learning, LLMs, prompt engineering, and agentic AI frameworks. This role requires strong expertise in Big Data processing, modern ML tools, and the ability to build scalable, production-ready data science solutions. Key Responsibilities Structured Data – Machine Learning & Analytics Build, deploy, and optimize ML models for predictive analytics, forecasting, classification, and regression. Perform large-scale feature engineering using PySpark and Big Data tools. Work on batch pipelines, model versioning, and experiment tracking. Develop cost estimation and risk/likelihood models using statistical and ML techniques. Text Data / NLP / GenAI Build NLP pipelines using deep learning frameworks such as PyTorch, TensorFlow, or similar. Develop real‑time, low‑latency inference systems for text classification, embeddings, semantic search, summarization, and retrieval. Create prompts, context graphs, and agentic workflows for LLM-based systems. Apply knowledge of prompt engineering, context engineering, and autonomous agent frameworks to production systems. Core Data Science Engineering & MLOps Work in Databricks for ETL, feature engineering, ML training, and orchestration. Use Azure services for model deployment, data pipelines, and infrastructure. Collaborate using Git-based workflows; leverage tools like GitHub Copilot, Claude Code, etc. Implement model monitoring, observability, drift detection, and performance tracking. Required Skills & Experience ✅ Core Skills Strong hands-on experience with Databricks (Delta Lake, MLflow, Job Orchestration). Excellent PySpark skills for large-scale distributed data processing. Proficiency in Azure cloud services (ADF, Azure ML, AKS, Databricks on Azure). Strong understanding of ML algorithms, statistical methods, and data analysis. Experience with deep learning frameworks: PyTorch TensorFlow Transformers (HuggingFace) Experience with model monitoring and ML observability. Ability to write clean, optimized code and leverage AI code assistants. ✅ NLP / GenAI Specific Skills Prompt engineering (task prompts, chain of thought, tool calling, retrieval prompts). Context engineering (retrieval pipelines, RAG, memory management, context structuring). Knowledge of LLM-based agentic frameworks (LangChain, Semantic Kernel, CrewAI, AutoGen, etc.). Experience with vector databases and embedding models is a plus. Good to Have Skills Experience with containerization (Docker, Kubernetes, AKS). Experience deploying models to production (REST APIs, real-time endpoints). Knowledge of streaming technologies (Kafka, EventHub, Spark Streaming). Understanding of CI/CD for ML (Azure DevOps / GitHub Actions). Who You Are A problem solver who is comfortable working with both structured and unstructured data. Someone who enjoys using modern AI tools to accelerate development. A data scientist who writes clean, production-grade code. A collaborator who thrives in cross-functional teams and fast-paced environments. Flexible work from home options available.

Responsibilities

The role involves building, deploying, and optimizing Machine Learning models for structured data tasks like forecasting and predictive analytics, alongside developing low-latency, real-time NLP/GenAI systems using deep learning and LLMs. Responsibilities also include core Data Science Engineering tasks using Databricks and Azure services for ETL, training, deployment, and MLOps.