Description

About Razorthink Razorthink is a software product company specializing in AI-powered demand forecasting solutions. Its flagship Demand Forecasting AI (DFAI) product helps businesses accurately predict sales demand across product lines—including new products with limited historical data—reducing overproduction and stock-outs while enabling granular buyer-level forecasting. The solutions are configurable, scalable, and designed for quick enterprise adoption. Job Summary Razorthink is seeking a motivated Data Scientist to join its Data & Analytics team and work on real-world Machine Learning, Deep Learning, NLP, and Generative AI solutions. The role involves end-to-end ownership of data science workflows—from data preparation and modeling to deployment and monitoring—under the mentorship of senior data scientists, following industry best practices for quality, reliability, and scalability. Key Responsibilities Modeling & Research Build, train, and evaluate ML models including classification, regression, and NLP models Work on text classification, NER, embeddings, and LLM prompting (RAG basics) Perform error analysis, A/B testing, and iterative model improvement Document experiments, results, and insights clearly Data Preparation & Feature Engineering Clean and prepare structured, semi-structured, and unstructured data Ensure data quality, reproducibility, and version control using Python and SQL Build reusable feature pipelines and prompt templates Production & MLOps Package ML models as APIs or batch jobs using FastAPI / Flask Containerize solutions using Docker Implement model monitoring for accuracy, latency, and data drift Track experiments and metrics using MLflow or Weights & Biases Collaboration & Communication Work closely with product managers and engineering teams to define success metrics Present analytical findings to technical and non-technical stakeholders Contribute to team documentation, code reviews, and best practices Minimum Qualifications Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Statistics, Engineering, or related fields Strong proficiency in Python and SQL Good understanding of statistics, experimentation, and ML fundamentals Hands-on experience with scikit-learn and basic exposure to PyTorch or TensorFlow Familiarity with NLP concepts (tokenization, embeddings, Transformers) Exposure to LangChain or LangGraph Working knowledge of Git, notebooks, and clear written/verbal communication Preferred (Nice to Have) Skills Experience with RAG architectures and vector databases (FAISS, Milvus, Pinecone) Exposure to LLM evaluation frameworks Data processing at scale using Pandas, Polars, Spark/PySpark Workflow orchestration tools like Airflow or Prefect Cloud exposure (AWS, GCP, or Azure) Participation in Kaggle, hackathons, open-source projects, or research work Success Metrics (First 90 Days) Deliver at least one ML / NLP / GenAI feature to staging or production Establish baseline metrics and maintain an experiment tracking system Close assigned bugs and technical debt with proper testing and documentation Tools & Technologies Python, SQL, scikit-learn, PyTorch, TensorFlow, Hugging Face, LangChain, LangGraph, Vector Databases, FastAPI, Docker, Git, MLflow, W&B, Airflow, Cloud Platforms (AWS/GCP/Azure)

Role and Responsibilities

• Key Responsibilities Modeling & Research Build, train, and evaluate ML models including classification, regression, and NLP models Work on text classification, NER, embeddings, and LLM prompting (RAG basics) Perform error analysis, A/B testing, and iterative model improvement Document experiments, results, and insights clearly Data Preparation & Feature Engineering Clean and prepare structured, semi-structured, and unstructured data Ensure data quality, reproducibility, and version control using Python and SQL Build reusable feature pipelines and prompt templates Production & MLOps Package ML models as APIs or batch jobs using FastAPI / Flask Containerize solutions using Docker Implement model monitoring for accuracy, latency, and data drift Track experiments and metrics using MLflow or Weights & Biases Collaboration & Communication Work closely with product managers and engineering teams to define success metrics Present analytical findings to technical and non-technical stakeholders Contribute to team documentation, code reviews, and best practices Minimum Qualifications Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Statistics, Engineering, or related fields Strong proficiency in Python and SQL Good understanding of statistics, experimentation, and ML fundamentals Hands-on experience with scikit-learn and basic exposure to PyTorch or TensorFlow Familiarity with NLP concepts (tokenization, embeddings, Transformers) Exposure to LangChain or LangGraph Working knowledge of Git, notebooks, and clear written/verbal communication