Description

About RazorThink RazorThink is an AI-driven software product company specializing in Demand Forecasting AI (DFAI) solutions. Its market-proven platform helps organizations accurately forecast product demand—including new products with limited historical data—while reducing stock-outs and overproduction. RazorThink’s solutions deliver explainable, granular forecasts aligned with each client’s unique business processes for rapid adoption and measurable impact. Job Summary RazorThink is looking for an early-career Data Scientist to join its Data & Analytics team and help design, build, and productionize Machine Learning, NLP, and Generative AI solutions. Under the mentorship of senior data scientists, you will work across the full model lifecycle—from data preparation and modeling to deployment and monitoring—while following best practices for quality, safety, and reliability. Key Responsibilities Modeling & Research Train, evaluate, and iterate on ML/DL/NLP models including classification, regression, NER, embeddings, and LLM-based workflows Perform error analysis, A/B testing, and model evaluation; document findings and insights Experiment with LLM prompting, RAG architectures, and transformer-based models Data Preparation & Feature Engineering Explore and prepare structured, semi-structured, and unstructured data using Python and SQL Ensure data quality, versioning, and reproducibility Build reusable feature pipelines and prompt templates Production & MLOps Package models as APIs or batch jobs using FastAPI/Flask Containerize applications using Docker and follow CI/CD best practices Implement monitoring for model accuracy, latency, and drift Maintain experiment tracking using MLflow or Weights & Biases (W&B) Collaboration & Communication Collaborate with product and engineering teams to define success metrics and delivery priorities Present analytical insights to both technical and non-technical stakeholders Contribute to documentation, testing, and technical debt resolution Minimum Qualifications Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Statistics, Engineering, or equivalent project/internship experience Strong proficiency in Python and SQL Solid understanding of statistics, probability, and experimentation Hands-on experience with scikit-learn and introductory PyTorch or TensorFlow Basic NLP knowledge including tokenization, embeddings, and transformer concepts Familiarity with Git, Jupyter notebooks, and clear written/verbal communication Preferred Qualifications (Nice to Have) Experience with RAG pipelines, vector databases (FAISS, Milvus, Pinecone) Exposure to LangChain or LangGraph and LLM evaluation frameworks Experience with data processing at scale (pandas, Polars, Spark/PySpark) Workflow orchestration tools such as Airflow or Prefect Cloud exposure (AWS, GCP, or Azure) Participation in hackathons, Kaggle competitions, open-source projects, or research publications Success Measures (First 90 Days) Deliver at least one ML/NLP or GenAI feature to staging or production Establish baseline metrics, monitoring, and experiment logs Resolve scoped bugs or technical debt items with proper testing and documentation Tools & Technologies Python, SQL, scikit-learn, PyTorch, TensorFlow, Hugging Face, LangChain, LangGraph, Vector Databases, FastAPI, Docker, Git, MLflow, Weights & Biases, Airflow, Prefect, AWS, GCP, Azure

Role and Responsibilities

• Key Responsibilities Modeling & Research Train, evaluate, and iterate on ML/DL/NLP models including classification, regression, NER, embeddings, and LLM-based workflows Perform error analysis, A/B testing, and model evaluation; document findings and insights Experiment with LLM prompting, RAG architectures, and transformer-based models Data Preparation & Feature Engineering Explore and prepare structured, semi-structured, and unstructured data using Python and SQL Ensure data quality, versioning, and reproducibility Build reusable feature pipelines and prompt templates Production & MLOps Package models as APIs or batch jobs using FastAPI/Flask Containerize applications using Docker and follow CI/CD best practices Implement monitoring for model accuracy, latency, and drift Maintain experiment tracking using MLflow or Weights & Biases (W&B) Collaboration & Communication Collaborate with product and engineering teams to define success metrics and delivery priorities Present analytical insights to both technical and non-technical stakeholders Contribute to documentation, testing, and technical debt resolution