Job Description
We are seeking a highly skilled and motivated Machine Learning Engineer to join our Innovation and FinTech Lab Development Team.
Responsibilities:
1. Model Development: Design, build, and optimize machine learning models and algorithms to tackle complex business problems using techniques like supervised and unsupervised learning, deep learning, and reinforcement learning.
2. Data Preprocessing: Ensure data quality and integrity by cleaning, preprocessing, and transforming raw data for training and testing machine learning models.
3. Feature Engineering: Extract and engineer relevant features from data to enhance predictive power and generalization of machine learning models.
4. Experimentation and Evaluation: Conduct thorough experimentation and evaluation of various machine learning models to identify the best-performing solutions.
5. Performance Tuning: Optimize and fine-tune machine learning models for optimal accuracy, efficiency, and scalability in production deployment.
6. Data Visualization: Create insightful visualizations of model outputs and data trends to facilitate better understanding and decision-making processes.
7. Model Deployment: Collaborate with software engineers and DevOps teams to ensure smooth deployment of machine learning models into production environments.
8. Monitoring and Maintenance: Monitor performance of deployed models, address issues, and update models as necessary to maintain accuracy and relevance.
9. Research and Innovation: Stay up-to-date with latest advancements in machine learning and related fields to explore and implement state-of-the-art techniques.
10. Collaboration: Work closely with cross-functional teams to define project requirements and ensure successful project execution.
Requirements:
1. Bachelor's or advanced degree in Computer Science, Engineering, Data Science, or a related field.
2. Proven experience as a Machine Learning Engineer, Data Scientist, or in a related role.
3. Proficiency in programming languages such as Python, R, or Java for implementing machine learning algorithms and data manipulation.
4. Strong understanding of machine learning concepts, frameworks, and libraries.
5. Familiarity with data visualization tools and techniques.
6. Experience with big data technologies and distributed computing is a plus.
7. Solid knowledge of statistics and probability theory.
8. Excellent problem-solving and analytical skills.
9. Strong communication and teamwork abilities.