Data Scientist

Data Scientist - Key Responsibilities

Build and optimize

classification, regression , and

forecasting models

using classical ML and deep learning techniques. Develop and deploy

deep learning architectures

including

LSTMs ,

transformers , and other sequence-based models for time-series, NLP, and anomaly detection. Design and implement

NLP pipelines

for text classification, semantic search, summarization, and question answering using transformer-based models (e.g., BERT, T5, GPT). Create

RAG (retrieval-augmented generation) pipelines

integrating LLMs with vector databases (e.g., FAISS, Pinecone, Weaviate) and document indexing frameworks. Apply and fine-tune

LLMs

(e.g., OpenAI, Mistral, LLaMA, Cohere) for domain-specific tasks using supervised fine-tuning or LoRA/QLoRA methods. Build and orchestrate

multi-agent AI systems

using frameworks like

LangGraph , CrewAI, or OpenAgents to support tool-using, autonomous agents for decision-making workflows. Collaborate with data engineers, product managers, and stakeholders to translate business needs into production-ready solutions. Mentor and support junior data scientists through code reviews, model design feedback, and collaborative experimentation. Promote best practices in reproducible modeling, responsible AI, and scalable deployment.

Required Skills & Experience

5+ years of experience in data science or applied machine learning, with a strong background in both classical and deep learning methods. Hands-on experience with

Python , and libraries/frameworks such as scikit-learn, pandas, PyTorch, TensorFlow, Hugging Face Transformers, and LangChain. Strong understanding of

classification metrics , feature engineering, model validation, and hyperparameter tuning. Demonstrated experience with

LLMs , including fine-tuning, prompt engineering, and

retrieval-augmented generation

techniques.