Senior Data Scientist

This role is for one of the Weekday's clients

Senior Data Scientist

machine learning, deep learning, and natural language processing (NLP)

Requirements

Key Responsibilities:

• Develop, implement, and optimize machine learning and AI models to address business challenges across various domains.
• Apply deep learning techniques for tasks such as image recognition, text processing, predictive analytics, and recommendation systems.
• Lead projects in natural language processing (NLP), including sentiment analysis, entity recognition, text classification, and conversational AI.
• Collaborate with cross-functional teams—including product managers, engineers, and domain experts—to identify opportunities where data science and AI can create measurable value.
• Conduct data exploration and analysis to extract meaningful insights from structured and unstructured datasets.
• Design, build, and maintain end-to-end pipelines for data collection, preprocessing, model training, evaluation, and deployment.
• Stay updated with the latest advancements in AI, ML, NLP, and deep learning, and recommend innovative approaches for their practical implementation.
• Mentor junior data scientists and analysts, providing guidance on best practices in data science, machine learning, and model evaluation.
• Communicate findings, insights, and recommendations clearly to both technical and non-technical stakeholders.
• Ensure models are scalable, explainable, and ethical, while addressing concerns of bias, fairness, and data privacy.
  

Required Skills and Qualifications:

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, or a related field.
• 2-6 years of hands-on experience in data science, machine learning, and AI-driven projects.
• Strong proficiency in programming languages such as Python or R, with expertise in libraries like TensorFlow, PyTorch, Scikit-learn, Keras, or similar frameworks.
• Solid understanding of deep learning architectures (CNNs, RNNs, LSTMs, Transformers, GANs) and their applications.
• Proven experience working with NLP techniques including tokenization, embeddings, attention mechanisms, and transformer-based models (e.g., BERT, GPT).
• Experience with data preprocessing, feature engineering, and statistical analysis.
• Familiarity with cloud-based platforms (AWS, Azure, GCP) for model training and deployment.
• Strong problem-solving skills and ability to translate business requirements into data science solutions.
• Excellent communication skills with the ability to present technical concepts to non-technical audiences.
  

Preferred Skills:

• Experience in deploying machine learning models in production environments.
• Familiarity with big data tools such as Spark, Hadoop, or Hive.
• Knowledge of MLOps practices for managing model lifecycle and performance monitoring.
• Exposure to reinforcement learning or other advanced AI subfields