Lead Data Scientist

Lead Data Scientist

• Lead and mentor a team of data scientists and machine learning engineers.
• Design, develop, and implement data science and machine learning solutions to solve complex business problems.
• Utilize deep learning and NLP techniques to extract insights from large datasets.
• Oversee the entire machine learning lifecycle, from data collection and preprocessing to model training, evaluation, and deployment.
• Collaborate with cross-functional teams to integrate machine learning models into production environments.
• Stay up-to-date with the latest advancements in data science, AI, and generative AI technologies.
• Ensure the scalability, reliability, and performance of machine learning models and systems.
• Develop and maintain documentation for data science processes, models, and algorithms.
• Provide technical leadership and guidance in the adoption of best practices for data science and machine learning.

Required

• Masters or PhD in Data Science, Mathematics, Statistics, Computer Science, or a related field.
• 8+ years of experience in data science, with a focus on deep learning and NLP.
• Proven experience in leading and managing data science teams.
• Strong proficiency in Python and experience with machine learning libraries such as TensorFlow, PyTorch, and scikit-learn.
• Hands-on experience with cloud platforms such as AWS or Azure.
  Knowledge of Flask or similar web frameworks.
• Experience with generative AI technologies is a plus.
  Solid understanding of statistical methods and mathematical concepts.
• Strong problem-solving skills and the ability to work in a fast-paced environment.
• Excellent communication and interpersonal skills.

Preferred Qualifications:

• Experience with big data technologies such as Hadoop, Spark, or similar.
• Knowledge of software development practices, version control (Git), and agile methodologies.
• Familiarity with data visualization tools and techniques.

Benefits:

• Competitive salary and performance-based bonuses.
• Flexible work hours and remote work options.
• Professional development opportunities and continuing education support.
• Collaborative and inclusive work environment.