Data Scientist

Role-Data Scientist

Exp-3-8 years

Location-Hyderabad

Description : Required Skills

Core Technical Competencies

Advanced Python Programming : Expertise in Python with production-level code quality, including OOP, API development, and best practices (linting, testing, documentation)

Machine Learning Mastery : Deep understanding and practical application of :

Classical ML algorithms (Random Forests, Gradient Boosting, SVM, clustering techniques)

Deep Learning frameworks (TensorFlow, Keras, PyTorch)

Time series forecasting and anomaly detection

Model evaluation, validation, and optimization techniques

Data Engineering : Experience with data pipelines, ETL processes, and handling large-scale datasets (TB+ scale)

Cloud Platforms : Hands-on deployment experience with at least one major cloud platform (AWS, Azure, GCP), including :

Managed ML services (SageMaker, Azure ML, Vertex AI)

Containerization and orchestration (Docker, Kubernetes)

Serverless architectures for ML deployment

Any or both of the NLP / ML Engineering skillsets is applicable.

NLP & Text Analytics

Experience with modern NLP techniques including transformer models (BERT, GPT)

Text preprocessing, feature extraction, and representation learning

Practical applications : sentiment analysis, document classification, named entity recognition

Working knowledge of NLP libraries (NLTK, spaCy, Hugging Face Transformers)

ML Engineering & Production Systems

MLOps practices : model versioning, monitoring, and automated retraining

Building scalable ML pipelines and APIs (FastAPI, Flask)

Experience with distributed computing frameworks (Spark / PySpark)

Performance optimization and model compression techniques

Desired Skills

Advanced AI / ML Capabilities

Generative AI & LLMs : Experience with LangChain, RAG architectures, prompt engineering, and fine-tuning large language models

Computer Vision : Document AI, OCR technologies, image classification using CNNs / YOLO

Recommendation Systems : Collaborative filtering, content-based filtering, hybrid approaches

Advanced Analytics : Causal inference, A / B testing, experimental design

Technical Stack

Big Data Tools : PySpark, Dask, or similar distributed computing frameworks

Visualization : Creating impactful dashboards using Tableau, Power BI, or Python libraries (Plotly, Dash)

Version Control & CI / CD : Git workflows, automated testing, and deployment pipelines

Database Systems : SQL proficiency, experience with NoSQL databases, vector databases