Euclid Innovations - Data Scientist

Data Scientist

Experience: 3 Years

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