Data Scientist
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Job Location:
Monthly Salary:
Posted on:
10 hours ago
Vacancies:
1 Vacancy
Job Summary
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
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
Key Skills
- Laboratory Experience
- Immunoassays
- Machine Learning
- Biochemistry
- Assays
- Research Experience
- Spectroscopy
- Research & Development
- cGMP
- Cell Culture
- Molecular Biology
- Data Analysis Skills
