Data Scientist ( Machine Learning) - Immediate Joiner

Job Title:

Location:

Department:

Experience:

Employment Type:

Job Summary

We are seeking a highly skilled Data Scientist with strong expertise in Machine Learning, Deep Learning, and Natural Language Processing (NLP). The ideal candidate will have hands-on experience designing, developing, and deploying AI/ML models that solve real-world problems, optimize decision-making, and drive innovation.

Key Responsibilities

Data Analysis & Preprocessing:

Collect, clean, transform, and analyze large, structured and unstructured datasets using Python, SQL, and data wrangling tools.

Model Development:

Build and optimize machine learning models (classification, regression, clustering, recommendation systems) using algorithms such as Random Forest, XGBoost, SVM, etc.

Deep Learning & NLP:

Design and train deep neural networks using frameworks like TensorFlow or PyTorch.

Work on NLP applications such as text classification, entity recognition, sentiment analysis, summarization, and large language models (LLMs).

Fine-tune transformer-based architectures (e.g., BERT, GPT, T5, LLaMA).

Feature Engineering & Model Evaluation:

Perform feature extraction, selection, and model validation using statistical and ML evaluation metrics.

Deployment & Integration:

Implement and deploy models into production using Docker, FastAPI, Flask, MLflow, or AWS Sagemaker.

Research & Innovation:

Stay up to date with latest advancements in AI/ML and NLP; contribute to POCs, patents, or research publications.

Collaboration & Communication:

Work closely with data engineers, product teams, and business stakeholders to translate analytical insights into strategic solutions.

Required Skills & Tools

Programming & Libraries:

Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, NLTK, SpaCy, Transformers)

SQL and data querying skills

Familiarity with R (optional)

Machine Learning:

Regression, Classification, Clustering, Dimensionality Reduction, Feature Engineering

Model selection, hyperparameter tuning, cross-validation

Deep Learning:

CNNs, RNNs, LSTMs, Transformers

Generative AI (LLMs, diffusion models, embeddings, prompt engineering preferred)

NLP:

Text preprocessing, tokenization, sentiment analysis, entity recognition

LLM fine-tuning, embeddings, vector databases (e.g., Pinecone, FAISS)

Tools & Platforms:

Git / GitHub, Jupyter, MLflow, Docker, Streamlit, FastAPI

Cloud Platforms: AWS / GCP / Azure

Versioning and CI/CD pipelines for model deployment

Education

Bachelors or Masters degree in Computer Science, Data Science, AI/ML, Statistics, or related field.