2 Senior Data Scientist Machine Learning Engineering jobs in whatjobs
Senior Data Scientist - Machine Learning Engineering
Posted 19 days ago
Job Viewed
Job Description
Our client is looking for a highly accomplished Senior Data Scientist specializing in Machine Learning Engineering to join their cutting-edge, fully remote data science team. This role is pivotal in developing, deploying, and scaling sophisticated machine learning models that drive critical business decisions and power innovative product features. You will work at the intersection of data science and software engineering, building robust MLOps pipelines, optimizing model performance, and ensuring the reliable productionization of AI solutions. The ideal candidate possesses a strong theoretical foundation in machine learning algorithms, coupled with practical experience in building and deploying ML systems in a cloud environment. You will collaborate with cross-functional teams, including product managers, data engineers, and other data scientists, to translate complex business challenges into data-driven solutions. This is an opportunity to lead impactful projects and shape the future of AI within the organization, all from the convenience of your home office.
Responsibilities:
Responsibilities:
- Design, develop, and implement advanced machine learning models and algorithms for various business applications.
- Build and maintain scalable, production-ready MLOps pipelines for model training, deployment, monitoring, and retraining.
- Collaborate with data engineers to ensure data quality, accessibility, and efficient processing for ML workflows.
- Optimize machine learning models for performance, accuracy, and resource utilization in cloud environments (e.g., AWS SageMaker, Azure ML, GCP AI Platform).
- Conduct rigorous experimentation and A/B testing to evaluate model effectiveness and identify areas for improvement.
- Stay abreast of the latest research and advancements in machine learning, deep learning, and AI, and apply them to solve business problems.
- Translate complex business requirements into technical specifications for ML solutions.
- Mentor junior data scientists and ML engineers, fostering a collaborative and knowledge-sharing environment.
- Effectively communicate complex technical findings and recommendations to both technical and non-technical stakeholders.
- Ensure the ethical development and deployment of AI systems, considering fairness, transparency, and bias mitigation.
- Contribute to the overall data science strategy and roadmap of the organization.
- Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field.
- 5+ years of experience in data science, with a significant focus on machine learning engineering and productionalization.
- Proven track record of developing and deploying machine learning models in production environments.
- Strong proficiency in programming languages such as Python (with libraries like scikit-learn, TensorFlow, PyTorch, Pandas).
- Extensive experience with cloud platforms and their ML services (e.g., AWS, Azure, GCP).
- Deep understanding of various ML algorithms, including supervised, unsupervised, and deep learning techniques.
- Experience with MLOps tools and practices (e.g., MLflow, Kubeflow, Docker, CI/CD).
- Excellent analytical, statistical, and problem-solving skills.
- Strong communication and collaboration abilities, with the capacity to work effectively in a remote team setting.
- Experience with big data technologies (e.g., Spark) is a plus.
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Senior Data Scientist - Machine Learning Engineering
Posted today
Job Viewed
Job Description
Our client is seeking a highly skilled and experienced Senior Data Scientist with a strong focus on Machine Learning Engineering to join their innovative, fully remote data science team. This role is critical for the development and deployment of production-grade machine learning models and systems that drive our data-driven products and decision-making. You will bridge the gap between research and engineering, building robust, scalable, and efficient ML pipelines.
Key Responsibilities:
Qualifications:
Key Responsibilities:
- Design, build, and maintain scalable machine learning pipelines for data processing, model training, evaluation, and deployment.
- Develop and implement MLOps best practices, including CI/CD for ML models, monitoring, and version control.
- Collaborate with data scientists and software engineers to operationalize machine learning models into production environments.
- Optimize machine learning models for performance, scalability, and resource efficiency.
- Develop and manage data infrastructure and feature stores required for ML applications.
- Implement robust testing and validation strategies for ML models and pipelines.
- Stay current with the latest advancements in ML engineering, MLOps, and relevant technologies.
- Troubleshoot and resolve issues related to ML model performance and production deployment in a remote setting.
- Contribute to the architectural design of ML systems and platforms.
- Mentor junior data scientists and engineers, promoting best practices in ML engineering.
Qualifications:
- Master's or Ph.D. in Computer Science, Data Science, Statistics, or a related quantitative field.
- A minimum of 6 years of experience in data science or machine learning, with a significant portion focused on ML engineering and production deployment.
- Proven experience in building and deploying machine learning models at scale using frameworks like TensorFlow, PyTorch, scikit-learn, or XGBoost.
- Strong proficiency in programming languages such as Python and associated data science/ML libraries.
- Experience with MLOps tools and platforms (e.g., MLflow, Kubeflow, SageMaker, Azure ML).
- Solid understanding of software engineering principles, CI/CD pipelines, and cloud platforms (AWS, Azure, GCP).
- Experience with big data technologies (e.g., Spark, Hadoop) and containerization (Docker, Kubernetes).
- Excellent problem-solving, analytical, and debugging skills.
- Strong communication and collaboration skills, essential for effective teamwork in a remote environment.
- Ability to work independently and manage complex projects from conception to deployment.
This advertiser has chosen not to accept applicants from your region.
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