Data Scientist

designed to improve the quality, continuity, and analytical usefulness of AMI interval data

across APC, GPC, and MPC. The platform will use AI/ML to perform data gap-fill, usage

reconstruction, and short-term/long-term forecasting, and enable downstream analytics.

The primary objective for this vendor engagement is to deliver a production-grade, scalable,

accurate ML-based gap-fill and forecasting engine that can be integrated into Southern

Company's data ecosystem.

2. Scope of Work, * Process 4.4M meters (15-min resolution) in batch production cycles

• Support parallelization / Spark-based architecture

4.3 Operational Expectations

• Runtime targets defined for daily and weekly pipelines

• Monitoring hooks for:

o Model drift

o Data Anomalies

o Input/outage alignment issues

5. Deliverables
6. ML Models & Codebase

o Gap-fill models (primary deliverable)

o Forecasting models

o Modular architecture for future extensions

2. Documentation

o Model(s) documentation

• No external data movement permitted

6.2 Governance

• Conform to Southern Company metadata, tagging, and logging standards

• Models must not be used for billing (analytics-only)

6.3 Vendor Collaboration Expectations

• Weekly progress meetings with Solomon, Joyce S. and project team

• Transparent issue escalation

• Ability to collaborate using Jira (AMI team instance)

• PM is already instated

• 1 Data Scientist from AMI DSA will work in this project

7. Evaluation Criteria for Vendors

• Technical strength and ML methodology

• Scalability and cloud architecture alignment

• Experience with utility AMI datasets

• Clarity of proposed MLOps approach

• Documentation quality

• Speed to delivery

• Total cost and licensing structure, 3-5 years of experience as a data scientist (or similar title)

3+ years of experience using AI/ML

Strong Python programming experience

Understanding/Experience within AMI data

Background within a utility

Experience with LLM (large language model)

Ability to deliver source code, notebooks, CI/CD scripts, and documentation Databricks