Data Scientist - INDIA

Vytwo•Prosper, TX

About The Position

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.

Requirements

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.
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.).

Nice To Haves

Experience with vector databases and embedding models is a plus.
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).

Responsibilities

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.
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.
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.