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# Lead Data Scientist

- **Company:** AGILE GRID SOLUTIONS LLC
- **Location:** Frisco, TX, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Geographic Information Systems, Artificial Intelligence, Amazon Web Services, Microsoft Azure, Supervisory Control and Data Acquisition (SCADA), Information Extraction, Data Intelligence, Python (Programming Language), Machine Learning, Operational Data Store, Standard Sql, Software Engineering, Large Language Models, Apache Spark, Model Validation, Performance Monitor, Machine Learning Operations, Software Coding, Databricks
- **Published:** October 29, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pvbqhrhkd5

## About the Role

You bring at least eight years of applied machine learning or data science experience and a record of building and shipping multiple models used by customers. You have set technical direction, selected tools and standards, and mentored or coached data scientists or engineers. You bring strong expertise in at least one area such as time-series forecasting, anomaly detection or optimization, supported by sound statistical knowledge, experimental design and model evaluation skills. Expert Python and SQL skills are required, as is a willingness to remain hands-on.

You also have working knowledge of LLM and generative AI systems, including evaluation approaches such as test-set design, metrics, human labeling and error analysis. You can work effectively with product, engineering, executives and domain experts, translate ambiguous challenges into measurable work, and communicate candidly when an approach is unlikely to succeed. Experience with fast-moving software development and responsible production delivery is important. Energy, oil and gas, SCADA/IIoT, geospatial data, document extraction, Databricks, Spark, Azure, AWS, MLflow, early-stage team building or customer-focused innovation work are beneficial, but not required. A quantitative degree or equivalent practical experience is expected; an advanced degree is welcome.

This is an in-office position in Frisco, Texas, or Calgary, Alberta. The US position is classified as exempt. Canadian employment classification will follow Alberta employment standards.

## Description

PakEnergy is seeking a hands-on Lead Data Scientist to establish and lead data science and machine learning for the Innovation Lab. Reporting to the Chief Product Officer, you will shape the Lab's data science direction while building models and writing code yourself. You will partner with experts across Land, Production, Accounting and Transportation to frame customer problems, assess opportunities and determine whether proposed solutions are supported by evidence. This role offers ownership from initial exploration through customer pilots, production handoff and ongoing model performance. You will also work closely with the Lead Applied AI Engineer, whose focus includes generative AI, LLMs and agentic systems. There are no direct reports initially, but the role can help shape and lead a future data science team as the Lab grows., Collaborate with product experts to define customer problems, establish measurable outcomes and prioritize promising opportunities. Explore and model production, SCADA/IIoT, accounting, transportation, geospatial and other operational data. Build forecasting, anomaly detection, optimization and predictive models, taking work from analysis to prototype. Design experiments, offline evaluations and pilot success measures, and make evidence-based recommendations about whether solutions should proceed.

Partner with the Lead Applied AI Engineer by supplying relevant data, contributing statistical and machine learning models, and helping design rigorous evaluations for AI systems. Work with solution engineers on customer pilots and define data, monitoring and MLOps requirements for production handoff. Collaborate with engineering after deployment to monitor performance, detect drift and improve models. Establish standards for data quality, reproducibility, experimentation and responsible machine learning; document practices, strengthen data fluency across the Lab, mentor teammates and contribute to future hiring plans.