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.