⭐ 1. Core Data Science Roles
➡️ Data Scientist
Technical Skills
- Statistics & probability
- Python/R
- Machine learning algorithms
- SQL
- Data cleaning & feature engineering
- Data visualization (Tableau/Power BI/Matplotlib/Seaborn)
Soft Skills
- Business understanding
- Problem-solving
- Communication & storytelling
➡️ Junior Data Scientist
Technical Skills
- Basics of Python/R
- Basic ML (regression, classification)
- Data preprocessing
- SQL fundamentals
- Basic visualization
Soft Skills
- Willingness to learn
- Ability to follow guidance
- Good documentation habits
➡️ Senior Data Scientist
Technical Skills
- Advanced ML & statistical modeling
- Model optimization & validation
- Deployment basics (APIs, containers)
- Experiment design (A/B testing)
- Big data tools (Spark/Hadoop)
Soft Skills
- Mentoring juniors
- Stakeholder communication
- Translating business needs into models
➡️ Lead / Principal Data Scientist
Technical Skills
- End-to-end ML system architecture
- Advanced deep learning knowledge
- Production-level model scaling
- Domain expertise (finance, healthcare, etc.)
Soft Skills
- Strategy & roadmap creation
- Cross-team leadership
- Decision-making at scale
📊 2. Analytics & Business Roles
➡️ Data Analyst
Technical Skills
- SQL & data extraction
- Excel + visualization tools
- Dashboard creation
- Basic statistics
Soft Skills
- Report writing
- Analytical thinking
➡️ Business / Product Analyst
Technical Skills
- A/B testing
- KPI design
- SQL + BI dashboards
- Product metrics
Soft Skills
- Business acumen
- Stakeholder management
🤖 3. Machine Learning & AI Roles
➡️ Machine Learning Engineer
Technical Skills
- Strong Python
- ML pipelines
- Model deployment (Docker, REST APIs)
- Cloud (AWS/Azure/GCP)
- CI/CD for ML
- Data structures & algorithms
Soft Skills
- Engineering mindset
- Collaboration with Data Scientists
➡️ Deep Learning Engineer
Technical Skills
- TensorFlow / PyTorch
- CNNs, RNNs, Transformers
- GPU computing
- Large datasets & distributed training
➡️ NLP Engineer
Technical Skills
- NLP fundamentals
- Transformer models (BERT, GPT)
- Tokenization, embeddings
- Text classification/generation
➡️ AI Research Scientist
Technical Skills
- Advanced math (linear algebra, optimization)
- Novel algorithm development
- Research publications & experimentation
- Prototyping in Python
🧹 4. Data Engineering & Infrastructure
➡️ Data Engineer
Technical Skills
- ETL/ELT pipelines
- SQL & NoSQL databases
- Big Data (Spark, Kafka)
- Cloud platforms
- Data warehousing
Soft Skills
- Reliability & system thinking
➡️ MLOps Engineer
Technical Skills
- CI/CD for ML
- Model monitoring
- Kubeflow, MLflow, Sagemaker
- Container orchestration (Kubernetes)
- Automation scripting
🗂️ 5. Data Architecture & Governance
➡️ Data Architect
Technical Skills
- Database design
- Cloud data architecture
- ETL strategy
- Data modeling (star/snowflake schema)
➡️ Data Governance / Quality Roles
Technical Skills
- Data lineage
- Data quality frameworks
- Metadata management
- Compliance (GDPR, HIPAA)
📦 6. Specialized Data Science Roles
➡️ Quantitative Analyst (Quant)
Technical Skills
- Advanced statistics
- Financial modeling
- Python + C++
- Time series modeling
➡️ Risk/Data Scientist
Technical Skills
- Risk modeling
- Regression, scoring models
- Domain knowledge (credit, insurance)
➡️ Healthcare/Geospatial Data Scientist
Technical Skills
- Domain-specific data tools
- Medical or geospatial modeling methods
📈 7. Leadership Roles
➡️ Data Science Manager
Skills
- Project management
- Recruiting & team development
- Roadmap planning
- Technical oversight
➡️ Director/Head of Data Science
Skills
- Strategy creation
- Cross-org decision-making
- Budgeting
- Innovation leadership
➡️ Chief Data Officer / Chief AI Officer
Skills
- Enterprise data strategy
- AI governance
- Executive communication
- Policy & high-level architecture