Overview:
The Machine Learning Engineer is crucial in developing and implementing machine learning solutions to improve our news aggregator app. You need to collaborate with crossfunctional teams to design and deploy models that enhance user experiences and operational efficiency.
Key Responsibilities:
- Utilize machine learning techniques to create predictive models
- Identify and analyze patterns in large datasets
- Develop and implement custom algorithms and statistical models
- Optimize model performance for scalability and efficiency
- Collaborate with software engineers to integrate models into production systems
- Conduct experiments to evaluate model effectiveness
- Stay updated with the latest advancements in machine learning and AI
- Contribute to the design and development of data infrastructure and pipelines
- Collaborate with stakeholders to understand business needs
- Communicate findings and insights effectively to nontechnical stakeholders
- Ensure adherence to data privacy and security regulations
- Participate in code reviews and provide constructive feedback
- Document models experiments and findings
- Mentor and guide junior team members
Required Qualifications:
- Bachelors or Masters degree in Computer Science Statistics Mathematics or a related field
- Proven experience in machine learning data analysis and statistical modeling
- Proficiency in programming languages such as Python R or Java
- Experience with machine learning libraries and frameworks (e.g. TensorFlow PyTorch scikitlearn)
- Strong understanding of deep learning algorithms and techniques
- Ability to translate business objectives into analytical solutions
- Experience in developing and deploying machine learning models in a production environment
- Solid understanding of data structures storage and processing
- Excellent problemsolving and analytical skills
- Strong communication and collaboration abilities
- Familiarity with cloud computing platforms (e.g. AWS Azure GCP)
- Knowledge of version control systems (e.g. Git)
- Experience with model optimization and performance tuning
- Understanding of ethical considerations in machine learning and AI
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