Lead Data Scientist
- Canada
Full-time
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Job details
- Job type
- Full-time
- Posting language
- English
- Posted
Original job description
PakEnergy is accelerating its innovation. Join the team building practical AI and data science solutions for the energy industry.
About The Company
More than 2,300 oil and gas companies rely on PakEnergy’s software for land management, production, accounting and transportation. PakEnergy is expanding its use of AI across these platforms through the PakEnergy Innovation Lab. The Lab brings together product experts, applied AI engineers and solution engineers to identify meaningful customer challenges, develop working prototypes using operational data, and bring validated solutions into production. Recent innovations include AI-powered lease provision analysis, PakCAPTURE document processing for transportation, and OnPing Plunger well optimization.
About The Role
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.
Qualifications
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.
Responsibilities
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.
Benefits
PakEnergy offers a casual business work environment, generous paid time off, employer-sponsored health benefits and a retirement savings plan, including a 401(k) in the United States. The role provides direct access to leadership, deep collaboration with industry experts and the opportunity to see your work reach customers.
Equal Opportunity
PakEnergy is an equal opportunity employer. We welcome applicants of all backgrounds and are committed to an inclusive hiring process. If you are excited about this opportunity but do not meet every qualification, we encourage you to apply.
About The Company
More than 2,300 oil and gas companies rely on PakEnergy’s software for land management, production, accounting and transportation. PakEnergy is expanding its use of AI across these platforms through the PakEnergy Innovation Lab. The Lab brings together product experts, applied AI engineers and solution engineers to identify meaningful customer challenges, develop working prototypes using operational data, and bring validated solutions into production. Recent innovations include AI-powered lease provision analysis, PakCAPTURE document processing for transportation, and OnPing Plunger well optimization.
About The Role
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.
Qualifications
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.
Responsibilities
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.
Benefits
PakEnergy offers a casual business work environment, generous paid time off, employer-sponsored health benefits and a retirement savings plan, including a 401(k) in the United States. The role provides direct access to leadership, deep collaboration with industry experts and the opportunity to see your work reach customers.
Equal Opportunity
PakEnergy is an equal opportunity employer. We welcome applicants of all backgrounds and are committed to an inclusive hiring process. If you are excited about this opportunity but do not meet every qualification, we encourage you to apply.
More about this job
Responsibilities
Define and prioritize customer problems with product experts, then build and evaluate forecasting, anomaly detection, optimization, and predictive models using operational data. Lead experimentation and customer pilots, support production handoffs and ongoing model monitoring, and establish data science standards while mentoring teammates and helping shape the future team.
Requirements
Requires at least eight years of applied machine learning or data science experience, a record of shipping customer-facing models, and expertise in at least one area such as forecasting, anomaly detection, or optimization. Candidates need expert Python and SQL skills, strong statistical and evaluation capabilities, working knowledge of generative AI and LLM evaluation, and experience setting technical direction; a quantitative degree or equivalent practical experience is expected.
Skills
- Applied Machine Learning
- Data Science
- Python
- SQL
- Time-Series Forecasting
- Anomaly Detection
- Optimization
- Statistical Analysis
- Experimental Design
- Model Evaluation
- Generative AI
- Large Language Models
- MLOps
- Data Quality
- Model Monitoring
- Technical Leadership
Benefits
- Paid Time Off
- Health Benefits
- Retirement Savings Plan
- 401(k)
Visa sponsorship
Not detected in the job text
Categories
- Data & Analytics
- Technology
- Software
- Energy
- Management & Leadership
Keywords
- Data Science
- Machine Learning
- Python
- SQL
- Time-Series Forecasting
- Anomaly Detection
- Optimization
- Predictive Modeling
- Statistical Analysis
- Experimental Design
- Model Evaluation
- Generative AI
- Large Language Models
- Agentic Systems
- SCADA
- IIoT
- Geospatial Data
- Document Extraction
- Databricks
- Spark
- Azure
- AWS
- MLflow
- MLOps
- Data Quality
- Reproducibility
- Model Monitoring
- Drift Detection
- Energy
- Oil and Gas