Strong foundation in machine learning: Deep understanding of supervised, unsupervised, and reinforcement learning techniques.
Expertise in NLP: Proficiency in natural language processing tasks such as text classification, sentiment analysis, and language generation.
Proficiency in Python: Strong programming skills in Python, including experience with popular machine learning libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
Big Data Processing: Experience with big data processing frameworks like PySpark and cloud platforms (e.g., AWS, GCP, Azure).
SQL: Knowledge of SQL for data querying and analysis.
Optimization Techniques: Understanding of optimization algorithms (e.g., gradient descent, stochastic gradient descent) and hyperparameter tuning.
Problem-solving skills: Ability to break down complex problems into smaller, manageable tasks.
Collaboration: Strong teamwork and communication skills to work effectively with cross-functional teams.
A track record of delivering impactful ML models and products.