Job Description

• Data Scientist Operations Improvement (Experience: 10+ years) JL 13-15
  • The hiring manager's preference "... is for data scientists with engineering degrees. Many of our investigations will be into industrial process problems (involving physics), and my experience has been that engineering graduates are best suited to those problems. "
• Education: Master's degree in any STEM discipline, or BSc Engineering + strong knowledge of statistics

Requirements:
Oil and gas, or mining industry experience (ideal candidate with oil sands and/or petroleum refining experience).
Strong background in statistics and machine learning.
Proven track record of solving operational problems through the application of statistics, ML, and other advanced analytics techniques. Will be asked to discuss work portfolio at interview.
Solid communication and interpersonal skills.
Fluent in Python and Python libraries related to statistics and machine learning.



Preferred extras:
Lean Six-Sigma training.
Mathematical optimization capabilities (LP, MIP, CP, genetic algorithms).

Q&A

• What is the main business problem(s) these guys will be tackling?
  • Process optimization
• Are they focused on predictive analytics, optimization, root cause analysis, process automation, or all of the above?
  • Potentially all of the above, and a lot of statistical analysis
• Is the work more hands-on coding and analysis, or will they also be expected to mentor/train others?
  • Hands-on coding and analysis
• Are they replacing someone, or is this a new role?
  • New role
• What data platforms or tools do you currently use (e.g., Databricks, Azure, AWS, on-prem, Snowflake)?
  • Azure, Databricks, Unity Catalog
• Which ML/statistics libraries are standard internally (scikit-learn, TensorFlow, PyTorch, etc.)?
  • Scikit-learn, panads, numpy and potential optimization libraries such as pyomo
• Any data engineering responsibilities, or is data prep handled elsewhere?
  • Potentially but most likely getting a snapshot of the data and work with that until it's ready for testing
• For the "oil and gas / mining" requirement, is oil sands and/or petroleum refining experience mandatory, or is broader O&G enough?
  • The more knowledge the better. But in general, someone with an engineering background such as process engineer or chemical engineer, would also help.
• How much field operations exposure do they need, versus just having modeled industrial processes from a distance?
  • It would be great if they have been exposed to field operations, but having modeled industrial processes from a distance is also good.

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