Sr Lead Data Science

Job Requirements
1. A minimum of 7 years of experience in data science, with a proven track record of delivering impactful data-driven solutions.
2. In-depth expertise in statistical analysis, machine learning algorithms, and predictive modeling techniques.
3. Strong programming skills in languages such as Python, etc., with experience in data manipulation, cleaning, and analysis.
4. Experience in leading and managing a team of data scientists, including setting goals, providing guidance and feedback, and ensuring project success.
5. Proven ability to effectively communicate complex technical concepts to both technical and non-technical stakeholders.
6. Strong problem-solving skills and the ability to think strategically to identify opportunities for leveraging data and analytics to drive business outcomes.
7. Familiarity with cloud platforms, such as DataBricks or Cloudera, and experience in deploying and managing data science models in a cloud environment.
8. Strong project management skills, with the ability to prioritize and manage multiple projects simultaneously. 

Qualifications
1. Master's or Ph.D. degree in a quantitative field such as Data Science, Computer Science, Statistics, Mathematics, or a related field.
2. Proven experience in leading and managing a team of data scientists, including hiring, training, and mentoring.
3. Strong domain knowledge in a specific industry or sector, such as semiconductor or material science.
4. Publications or research experience in the field of data science, machine learning, or related areas.
5. Experience in working with unstructured data, such as text or image data, and applying natural language processing or computer vision techniques.
6. Knowledge of advanced analytics techniques, such as deep learning or reinforcement learning.
7. Experience in working with data engineering teams to build and optimize data pipelines for data processing and analysis.
8. Familiarity with data privacy and security regulations and ensuring compliance in data science projects.
9. Strong business acumen and the ability to understand and align data science initiatives with business goals and objectives.
10. Active participation in data science communities, conferences, or meetups, and a passion for staying up-to-date with the latest advancements in the field