Data Scientist

Job Title: Data Scientist

Skills: Artificial intelligence, Machine Learning, NLP, Gen AI, Python, Rest API, Agentic AI, LLM, RAG, Devops and AWS

Experience: 4+ years

Location: Pune and Hyderabad

Duration: Full time

We at Coforge are hiring for Data Scientist role with following skill sets:

LLM & Generative AI

• Design, build, and deploy LLM-powered applications using frameworks such as LangChain, LlamaIndex, or OpenAI API.
• Develop and optimize prompt engineering strategies (few-shot, chain-of-thought, RAG) to improve the accuracy, consistency, and reliability of LLM outputs.
• Implement Retrieval-Augmented Generation (RAG) pipelines using vector databases (e.g., FAISS, Pinecone, Chroma, Weaviate).
• Fine-tune pre-trained LLMs (e.g., GPT, LLaMA, Mistral, Falcon, Claude,Gemini) on domain-specific datasets.
• Validate and structure LLM outputs using Pydantic models and output parsers to ensure data integrity.

Natural Language Processing (NLP)

• Build end-to-end NLP pipelines for real-world tasks including:
• Named Entity Recognition (NER)
• Text Classification & Sentiment Analysis
• Information & Data Extraction from Documents
• Document Summarization & Question Answering
• Semantic Search & Document Similarity
• Work with the Hugging Face Transformers ecosystem to leverage and fine-tune pre-trained models (BERT, RoBERTa, T5, etc.).
• Process large-scale unstructured text data from various sources such as PDFs, emails, scanned documents (OCR), and web content.

Anomaly Detection

• Design and implement anomaly detection systems for various domains, including:
• Financial fraud detection (unusual transactions, payment anomalies).
• Operational anomalies (system logs, network traffic, sensor data).
• Text-based anomalies (unusual document patterns, suspicious NLP signals).
• Apply a wide range of anomaly detection techniques including:
• Statistical Methods: Z-score, IQR, CUSUM.
• ML-based Methods: Isolation Forest, One-Class SVM, Local Outlier Factor (LOF).
• Deep Learning Methods: Autoencoders, LSTM-based sequence anomaly detection, Variational Autoencoders (VAEs).
• Time-Series Methods: ARIMA, Prophet, Seasonal Decomposition.
• Build real-time and batch anomaly detection pipelines that can scale to large datasets.
• Define and tune detection thresholds and alert mechanisms in collaboration with business and operations teams.

Machine Learning (ML)

• Design, train, evaluate, and deploy supervised and unsupervised machine learning models.
• Perform feature engineering, model selection, hyperparameter tuning, and cross-validation.
• Build and maintain end-to-end ML pipelines from data ingestion to model serving.
• Monitor model performance in production and implement retraining strategies to address data drift and model decay.
• Communicate model results, performance metrics, and business impact to technical and non-technical stakeholders.

Python & Software Engineering

• Write clean, modular, production-quality, and well-documented Python code.
• Build and expose ML models as REST APIs using FastAPI or Flask.
• Collaborate with MLOps/DevOps engineers to containerize (Docker) and deploy models in cloud environments.
• Follow best practices in version control (Git), testing, and CI/CD pipelines.

Data & Analytics

• Perform Exploratory Data Analysis (EDA) on structured and unstructured datasets to identify patterns, trends, and anomalies.
• Work with data from relational databases (SQL), data lakes, and cloud storage solutions.
• Create compelling and clear data visualizations (Matplotlib, Seaborn, Plotly) to communicate findings.