I. Data Analytics Services Consultants(s) shall be responsible for delivering end-to-end data analytics capabilities, including but not limited to: 1. Data Strategy and Governance a. Design and implementation of scalable data architectures b. Assessment and improvement of data quality c. Development of data governance frameworks and policies 2. Data Collection and Integration a. Design and deployment of ETL (Extract, Transform, Load) pipelines b. Integration of structured and unstructured data from diverse sources (e.g., APIs, databases, cloud platforms) 3. Descriptive and Diagnostic Analytics a. Development of interactive dashboards and reports using tools such as Power BI or Tableau b. Execution of root cause analyses and performance tracking through KPIs 4. Predictive and Prescriptive Analytics a. Construction of forecasting models to support data-driven decision-making b. Implementation of optimization models and scenario-based simulations II. Artificial Intelligence Services Consultant(s) shall provide AI services that align with SAWS’ strategic objectives, including but not limited to: 1. AI Strategy and Road mapping a. Identification and prioritization of AI use cases aligned with business goals b. Feasibility studies and return-on-investment (ROI) analysis 2. Natural Language Processing (NLP) a. Development of AI solutions such as chatbots, sentiment analysis engines, and document summarization tools b. Copilot (ChatGPT Style) ad-hoc analysis using NLP from a community of thought workers. c. Integration of all structured and non-structured data assets in which can be utilized in the Microsoft Ecosystem (such as Teams, Outlook, SharePoint, Loop, and other sources of data in which information can be retrieved from new knowledge bases. 3. Computer Vision a. Implementation of image classification, object detection, and optical character recognition (OCR) systems b. Implementation of image processing to replace data entry within field operations for capturing meter, well, pump, and similar data sources from our operational networks that can feed ingestion pipelines. c. QA/QC of large data sets of images and IoT data to escalate anomalieshuman operators for special handling and training models for future handling. 4. AI Model Deployment a. Establishment of Machine Learning Operations (MLOps) pipelines for Continuous Integration and Delivery (CI/CD) of machine learning models b. Design of model monitoring, retraining, and lifecycle management strategies III. Machine Learning (ML) Services Consultant(s) shall support the full lifecycle of machine learning initiatives, including but not limited to: 1. Model Development a. Design and training of supervised, unsupervised, and reinforcement learning models b. Execution of feature engineering and model selection processes 2. Model Evaluation and Tuning a. Application of cross-validation techniques and hyperparameter optimization b. Analysis of model bias, fairness, and performance metrics 3. Custom Machine Learning Solutions a. Development of tailored solutions such as recommendation engines, anomaly detection systems, and time series forecasting models
