Senior Data Scientist
Posted 3 days ago by Ampstek
Negotiable
Undetermined
Undetermined
London Area, United Kingdom
Summary: The Senior Data Scientist role involves collaborating with stakeholders to define business problems and project scopes for AI models. The position requires expertise in data acquisition, model selection, evaluation, prompt engineering, and deployment, ensuring effective communication and collaboration with team members. The candidate will also focus on optimizing model performance and monitoring its deployment in production environments.
Key Responsibilities:
- Work with stakeholders to understand business problems and define project scope.
- Design data pipelines for data acquisition, cleaning, and preprocessing.
- Design training strategies and select appropriate ML algorithms and architectures.
- Recommend metrics and design reports for model evaluation and optimization.
- Design prompts for effective communication with LLMs and evaluate their performance.
- Collaborate with engineers for model deployment and monitoring.
- Communicate findings and recommendations to stakeholders.
Key Skills:
- Experience in AI and machine learning methodologies.
- Proficiency in data preprocessing and pipeline design.
- Knowledge of ML algorithms and architectures, particularly LLMs.
- Ability to evaluate model performance using various metrics.
- Strong collaboration and communication skills.
Salary (Rate): undetermined
City: London Area
Country: United Kingdom
Working Arrangements: undetermined
IR35 Status: undetermined
Seniority Level: undetermined
Industry: Other
Detailed Description From Employer:
Responsibilities:
- Business Understanding and Scope Definition: Work with stakeholders to understand the business problem that the AI model aims to solve. Help define the project scope, translating business requirements into technical specifications. identify relevant data sources and determine key performance indicators (KPIs).
- Data Acquisition and Preprocessing: Work with ML engineers in designing pipelines collecting appropriate data from various sources, cleaning and preprocessing the data, and ensuring data quality.
- Model Selection and Training: Design appropriate training strategies (e.g., supervised learning, reinforcement learning) and appropriate configuring of model parameters. Design and select appropriate ML algorithms and architecture (LLM architecture (e.g., BERT, GPT-3) based on project requirements.
- Evaluation and Optimization: Recommend the metrics and design reports used to evaluate the model’s performance using various metrics, such as accuracy, precision, recall, and F1-score. Identify areas for improvement and optimize the model by adjusting parameters, trying different architectures, or incorporating new data.
- Prompt Engineering and Interaction Design: Designing prompts that effectively communicate with the LLM and elicit the desired responses Phrase prompts to get the best results and avoid unintended consequences. Experiment with different prompts and evaluate their impact on the LLM's performance.
- Deployment and Monitoring: Work with Engineers to deploy the AI model into a production environment. Recommend the metrics and reports to be used to track model performance. Contribute to the setting up of automated monitoring systems and developing strategies for handling unexpected behaviour.
- Collaboration and Communication: Collaborate with other team members, including ML engineers, product managers, and domain experts. Communicate their findings and recommendations to stakeholders.