Data Scientist 3 (IT) 4P/367

4P Consulting Inc.; Birmingham (AL); Hybrid; USD 90,000 - 120,000; Full time

Job summary

A consulting firm in Birmingham, AL is seeking a Data Scientist (Level 3) with 5-10 years of experience in advanced data analytics. The role involves leveraging big data tools and statistical modeling to support critical decision-making in the utilities industry. The successful candidate will work in a hybrid environment and collaborate with cross-functional teams to identify high-value data science opportunities.

Qualifications

• 5–10 years of applied data science experience in an enterprise setting.
• Utilities or energy-sector experience preferred.
• Proven success in designing, deploying, and scaling analytics models.

Responsibilities

• Acquire, clean, and process datasets from various sources.
• Build, validate, and deploy machine learning models.
• Develop data visualizations and dashboards.
• Utilize big data frameworks for analysis.
• Collaborate with cross-functional teams for data opportunities.

Skills

Python

Machine learning frameworks

Statistical knowledge

SQL

Data visualization

Tools

Power BI

Tableau

Spark

Hadoop

Databricks

Job description

Data Scientist 3

Location: Birmingham, AL 35203

Client- Alabama Power

Contract- 1 Year

Job Summary

We are seeking an experienced Data Scientist (Level 3) with 5–10 years of advanced data analytics experience to join our team. This role requires a strong foundation in statistics, machine learning, and data engineering. The successful candidate will leverage big data tools, programming expertise, and statistical modeling to develop insights and support critical decision-making in the utilities industry.

Key Responsibilities

• Data Wrangling & Exploration
  • Acquire, clean, and process large, complex datasets from multiple sources, including smart meters, grid sensors, customer systems, and financial platforms.
  • Explore data for anomalies, trends, and correlations to support operational, engineering, and business use cases.
• Model Development
  • Build, validate, and deploy machine learning and statistical models for applications such as predictive maintenance, outage prediction, customer load forecasting, and energy demand analysis.
  • Evaluate and apply appropriate ML algorithms including regression, classification, clustering, and time-series forecasting.
• Visualization & Reporting
  • Develop intuitive data visualizations and dashboards using tools like Power BI, Tableau, or Python libraries (Matplotlib, Seaborn, Plotly).
  • Translate complex results into clear business insights for engineering, operations, and leadership teams.
• Big Data & Advanced Analytics
  • Utilize big data frameworks such as Spark, Hadoop, or Databricks to process and analyze large-scale structured and unstructured datasets.
  • Support integration of analytics into operational systems, ensuring scalability and performance.
• Collaboration
  • Partner with cross-functional stakeholders in grid operations, transmission, distribution, and customer solutions to identify high-value data science opportunities.
  • Work closely with data engineers, business analysts, and domain experts to align models with business and regulatory needs.

Experience:

• 5–10 years of applied data science experience in an enterprise setting.
• Proven success in designing, deploying, and scaling advanced analytics models.
• Utilities or energy-sector experience strongly preferred (e.g., transmission, distribution, smart grid, AMI/MDM, renewable integration).

Technical Skills:

• Proficient in Python or R (statistical programming).
• Strong statistical knowledge and experience applying the scientific method.
• Hands-on experience with machine learning frameworks (scikit-learn, TensorFlow, PyTorch).
• Skilled in SQL and database querying.
• Experience with big data tools (Spark, Hadoop, Databricks) is highly desirable.
• Familiarity with cloud platforms (Azure, AWS, or GCP).

Soft Skills:

• Strong problem-solving and analytical abilities.
• Ability to present technical findings to non-technical stakeholders.
• Self-motivated, with capacity to work independently and collaboratively.

Work Environment

• Hybrid work arrangement with periodic travel to company sites, data centers, or field locations.
• Must be able to balance multiple concurrent projects in a fast-paced, results-driven environment.