Data Scientist

Razorthink; Bengaluru; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

Razorthink is seeking a Data & Analytics specialist to build and productionize ML/DL/NLP/GenAI solutions. You’ll handle data prep, modeling, evaluation, deployment, and monitoring under guidance from senior scientists.

You’ll collaborate with product and engineering to define metrics, run experiments, and present findings to both technical and non-technical audiences while learning best practices for quality, safety, and reliability.

Qualifications

• Bachelor’s or Master’s in CS, Data Science, Math, Stats, EE or equivalent project/internship experience.
• Proficient in Python and SQL; strong grasp of statistics and experimentation.
• Hands-on ML with scikit‑learn and introductory PyTorch/TensorFlow.

Responsibilities

• Train and evaluate models (classification, regression, text classification/NER, embeddings, LLM prompting/RAG basics).
• Explore and prepare structured/semi-structured/unstructured text using Python/SQL; ensure data quality and reproducibility.
• Package models as APIs/batch jobs with FastAPI/Flask and Docker; write unit/integration tests.

Education

Bachelor's / Master's in CS, Data Science, Math, Stats, EE

Tools

scikit-learn

PyTorch

TensorFlow

Hugging Face

LangChain

LangGraph

vector stores

FastAPI

Docker

MLflow/W&B

Job description

Join our Data & Analytics team to build and productionize ML/DL/NLP/GenAI solutions under the guidance of senior scientists. Youll work end-to-enddata prep, modeling, evaluation, deployment, and monitoringwhile learning best practices for quality, safety, and reliability.

What Youll Do

Modeling & Research

• Train and evaluate models (classification, regression, text classification/NER, embeddings, LLM prompting/RAG basics).
• Perform error analysis and A/B tests; document results and iterate quickly.

Data & Features

• Explore and prepare structured/semi-structured/unstructured text using Python/SQL; ensure data quality and reproducibility.
• Build reusable feature pipelines and prompt templates.

Production & MLOps

• Package models as APIs/batch jobs with FastAPI/Flask and Docker; write unit/integration tests.
• Add monitoring for accuracy/latency/drift; maintain experiment logs (MLflow/W&B).

Collaboration & Communication

• Work with product/engineering to define metrics and prioritize work; present findings to technical and non-technical audiences.

Minimum Qualifications

• Bachelors/Masters in a quantitative field (CS, Data Science, Math, Stats, EE) or equivalent project/internship experience.
• Proficient in Python and SQL; strong grasp of statistics and experimentation.
• Hands‑on ML with scikit‑learn and introductory PyTorch/TensorFlow.
• Basic NLP (tokenization, embeddings, Transformers familiarity) and exposure to LangChain or LangGraph.
• Git, notebooks, and clear written/verbal communication.

Preferred (Nice to Have)

• Projects/internships using RAG, vector databases (FAISS/Milvus/Pinecone), or LLM eval frameworks.
• Data processing at scale (pandas/Polars; Spark/PySpark basics), orchestration (Airflow/Prefect).
• Cloud familiarity (AWS/GCP/Azure), Docker, and simple API development (FastAPI).
• Participation in hackathons/Kaggle/OSS or research publications.

Success Measures (First 90 Days)

• Ship at least one ML/NLP or GenAI feature to staging/production with tests, docs, and monitoring.
• Establish baseline metrics and an experiment log; close a set of scoped bugs/tech-debt items.

Tools & Tech You May Use

• Python, SQL, scikit-learn, PyTorch/TensorFlow, Hugging Face, LangChain, LangGraph, vector stores, FastAPI, Git, Docker, MLflow/W&B, Airflow/Prefect, cloud services (AWS/GCP/Azure).