Description
- Data Science and Generative AI — Skills and Features (basic to advanced, 3–Foundations of Data Science — Statistics, probability engineering that, data cleaning, EDA, and feature form the baseline for all experience levels.
- Machine Learning Algorithms — Supervised and unsupervised models (regression, treess) and model selection, clustering, SVM/validation techniques.
- Deep Learning — Neural architectures (CNNs, RNNs, transformers, and representation) for vision, sequence learning. Towards Data Science
- Data Engineering — ETL/ELT, data lakes/warehouses, streaming, and scalable preprocessing for production ML.
- Model Evaluation — CI/CD for models and MLOps detection, reproducibility, and deployment, monitoring, drift pipelines.
- Feature Stores and Experimentation — Centralized feature management, experiment tracking, and hyperparameter tuning.
- Generative — LLMs, diffusion Models models, GANs, and VAEs used for text, image, audio, and synthetic data generation.
- Prompt Engineering — Designing prompts, few‑shot examples, and chain‑of‑thought strategies to steer LLM outputs effectively. Data and Data Aug
- Syntheticmentation — Generating labeled data to address class imbalance needs.
- Mult, privacy, and simulation — Combining textimodal AI models for richer, image, and audio and cross‑modal representations tasks.
- Explainability and Responsible — SHAP/LIME, fairness testing, bias mitigation AI, and governance models.
- for trustworthyScalable Training and Optimizationput tradeoffs. — Distributed training, mixed precision, model compression, and latency/through- Retrieval Augmented Generation — Combining vector stores and LLMs to ground responses in external knowledge and reduce hallucinations.
- Tooling and Framework PyTorch/TensorFlows, Hugging Face, Lang — Proficiency with, and cloud ML services.
- EvaluationChain, data platforms for Generative Systems — Human‑in‑the‑loop metrics, automated quality checks, and safety filters for content generation, Security, and Compliance.
- Privacy, access controls, model watermark — Differential privacying, and regulatory considerations.
- Business Impact and Productization — Translating models into features, ROI measurement, A/B testing, and stakeholder alignment.
- Leadership and Strategy — For senior candidates, building data science responsibly.
: shaping AI strategy teams, governance, and scaling AI—
