Data Scientist
Background :
A talented and experienced Data Scientist with a strong background in neural networks and graph theory to join our dynamic team. The ideal candidate will be passionate about leveraging advanced analytics techniques to extract meaningful insights from complex datasets. As a Data Scientist, you will play a crucial role in developing and implementing cutting-edge solutions to solve business problems and drive innovation.
Responsibilities:
Data Analysis and Modeling:
Utilize advanced statistical and machine learning techniques to analyze large datasets.
Design, implement, and evaluate predictive models using neural networks and graph theory.
Neural Network Development:
Develop and implement neural network models for various applications, including but not limited to image recognition, natural language processing, and time-series analysis.
Stay abreast of the latest advancements in neural network architectures and contribute to the continuous improvement of our modeling techniques.
Graph Theory Applications:
Apply graph theory to model and analyze complex relationships within datasets.
Develop algorithms and models for network analysis, community detection, and anomaly detection using graph theory.
Data Pre-processing:
Clean, pre-process, and transform raw data to prepare it for analysis.
Work with large-scale datasets and integrate data from multiple sources.
Collaboration:
Collaborate with cross-functional teams to understand business requirements and translate them into analytical solutions.
Communicate findings and insights to both technical and non-technical stakeholders.
Model Deployment and Maintenance:
Deploy models into production environments and monitor their performance.
Implement strategies for model maintenance and continuous improvement.
Data Visualization:
Create compelling visualizations to communicate insights and findings to both technical and non-technical audiences.
Utilize tools such as Tableau, Power BI, or other relevant platforms.
Requirements:
Bachelor / Master's or Ph.D. in a quantitative field such as Computer Science, Statistics, Mathematics, or a related discipline.
Proven experience of 6 years as a Data Scientist with a focus on neural networks and graph theory.
Strong programming skills in languages such as Python, R, or similar.
Experience with relevant libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
Proficiency in graph theory concepts and applications.
Solid understanding of statistical analysis, hypothesis testing, and experimental design.
Excellent problem-solving skills and the ability to think critically about complex business challenges.
Strong communication skills, with the ability to convey technical concepts to both technical and non-technical audiences.
Experience with big data technologies (e.g., Apache Spark) is a plus.
A portfolio or examples of projects demonstrating expertise in neural networks and graph theory.
Experience with cloud computing platforms (e.g., AWS, Azure, Google Cloud).
• Knowledge of regulatory requirements and compliance in the life sciences industry
