1. Research & Development
- Conduct thorough research of scientific publications to identify cutting-edge AI methodologies and techniques applicable to real-world problems.
- Design and execute experiments to validate hypotheses and improve AI models.
- Develop innovative algorithms and models to solve specific business challenges.
- Transform research findings into practical prototypes and products.
- Collaborate with interdisciplinary teams to apply AI research outcomes to production systems.
- Evaluate and refine existing models to improve performance and accuracy.
- Analyze large datasets to derive actionable insights and improve existing systems.
2. Implementation & Application
- Develop and implement machine learning models and algorithms for real-world applications.
- Build and optimize AI solutions using statistical techniques and machine learning frameworks.
- Conduct experiments to validate the effectiveness of models and algorithms in practical scenarios.
- Collaborate with cross-functional teams, including product managers and engineers, to integrate solutions into products.
- Extract meaningful insights from diverse data types such as images, videos, and text.
- Ensure research can be translated into practical applications for product development.
3. Knowledge Sharing & Collaboration
- Publish research findings in peer-reviewed journals and present at conferences.
- Stay updated with the latest advancements in AI and machine learning.
- Mentor junior researchers and contribute to the development of research proposals.
- Communicate complex technical concepts to non-technical stakeholders.
- Collaborate with academic institutions and industry partners on research projects.
What You Need To Maximize Your Contribution
1. Education & Experience
- BsC/Master or equivalent experience in Computer Science, Data Science, Machine Learning, Artificial Intelligent or a related technical field.
- 3+ years of experience in AI research with a strong track record of delivering applied solutions.
- Demonstrable experience in AI research, preferably with publications in renowned journals.
- Experience working with various AI techniques, including supervised and unsupervised learning, reinforcement learning, and neural networks.
2. Technical Skills
- Strong proficiency in programming languages such as Python, R.
- Deep understanding of machine learning algorithms, neural networks, and computational statistics.
- Experience with statistical analysis and experimental design.
- Proficiency in data manipulation and analysis using tools like SQL and Pandas.
- Expertise with machine learning libraries and frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Knowledge of software engineering principles and practices (optional).
3. Soft Skills
- Strong analytical and problem-solving skills.
- Excellent communication skills to convey complex findings to both technical and non-technical audiences.
- Ability to work independently and collaboratively within a team environment.
- Creativity and curiosity with a willingness to explore new ideas and approaches.
- Critical thinking skills to tackle complex challenges.
- Passion for continuous learning and adapting to new technological advancements.
