
Data Science and Machine Learning Expert
1 week ago
This position is for a data science and machine learning expert to help design, implement and manage advanced analytics tools in addition to preparing modern data platforms to gain insights from key business data. The employee will be responsible for leading all the processes from data collection, cleaning, and preprocessing, to training models and deploying them to production.The key responsibilities will be to prepare modern data platforms to gain insights from key business data. The employee will be responsible for designing, implementing and managing advanced analytics tools.
Key Responsibilities:- Work in tandem with engineering partners to identify and implement the most optimal cloud-based solutions for the company.
- Research, design and adopt cutting-edge algorithms to address real-world challenges such as summarization.
- Define and document best practices and strategies regarding deployment and infrastructure maintenance.
- Ensure model performance, uptime, and scale, maintaining high standards of code quality and thoughtful design.
- Maintain a platform that automates creating, training, deploying and updating machine learning models.
- 8-12 years of production model deployment experience for AI/ML solutions.
- Designing, developing, and researching machine learning systems, models, and schemes through data science and data analytics prototypes.
- Understanding of natural language processing (NLP), natural language understanding (NLU) and natural language generation (NLG).
- Develop and implement technical efforts to design, build, and deploy AWS ML models, including large-scale data processing, computationally intensive statistical modeling, and advanced analytics.
- Familiarity with deep learning, machine learning and NLP/NLG frameworks (like Keras, TensorFlow or PyTorch etc), HuggingFace Transformers and libraries like scikit-learn, spacy, gensim, CoreNLP etc.
- Should have experience in AWS services such as SageMaker, Elasticsearch, EC2, and general knowledge of AWS architecture & other services.
- Ability to write robust code in Python, Java and R.
- Experience optimizing model hyperparameter tuning for speed and cost.
- Experience in building MLOps pipeline using MLOps frameworks like Kubeflow, MLFlow, and DataRobot.
- Experience with Docker and Kubernetes.
- Familiarity with Eventbridge and AWS step function for workload orchestration.
- Experience with Agile Development.
- Good experience in process improvement/change management methods.
- Knowledge of various aspects of an enterprise technology architecture like business, information, data, network, and security.
- Understanding and knowledge of system development life cycle methodologies (such as waterfall, agile software development, rapid prototyping, and DevOps).
- Understanding and knowledge of IT service management (ITSM) and Information Technology Infrastructure Library (ITIL).
- Excellent analytical, planning, organizational and technical skills.
- Skilled at influencing, guiding, and facilitating stakeholders and peers with decision making.
- Ability to articulate new ideas and concepts to technical and nontechnical audiences.
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