Data Scientist ( Classic ML - Immediate Joiner)

Job Title: Data Scientist Classic ML, Deep Learning & NLP (Immediate Joiner)

Location: Gurugram/Bangalore

Department: Data Science / AI & Analytics

Experience: 3-5 years

Employment Type: Full-Time

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 Master’s degree in Computer Science, Data Science, AI/ML, Statistics, or related field.