Senior Data Scientist
Full-Time | Calgary-Based (hybrid)
About Us
Arolytics is on a mission to improve operational efficiency in oil and gas through the lens of strategic emissions management. We focus on operational efficiency in oil and gas through the lens of strategic emissions management. Our AroIQ platform turns raw SCADA signals into emission event detection, causation analysis, and volume quantification for major North American energy companies.
We are in a period of rapid expansion — several new upstream and midstream customers are onboarding, spanning tank farms, flaring and combustion, compressor stations, and heavy oil.
How We Work
We are Rigorous
Our models produce numbers that show up in regulatory filings and board-level emissions reports. Every value needs to be defensible, reproducible, and physically plausible. We don't ship "close enough."
We are Pragmatic
We work with real industrial process data, gapped, noisy, mislabeled, and inconsistent. Elegant theory is nice; working solutions for messy data are better.
We are Curious
The problems we solve sit at the intersection of thermodynamics, signal processing, and machine learning. No single discipline has the full answer. We need people who cross boundaries and ask good questions.
About the Role
This is a senior, hands-on role for someone who can own a customer engagement end to end: take a facility's raw SCADA history, work out how that facility physically operates, and build the detection, causation, and quantification models that hold up against it.
You will work alongside our Lead Data Scientist as a peer rather than under close supervision, and directly with emissions engineers, data engineers, and product leadership. You will also be in front of customers — talking to the operator's own facilities and process engineers about what the data shows.
What You'll Do
Facility & Process Understanding
- Read P&IDs and process flow diagrams and work out how a site actually operates
- Reason from a SCADA trace back to the physical process producing it
- Identify the emission pathways at a facility — what vents, what is recovered, what is combusted, and under what conditions
- Apply mass balance, pressure-volume relationships, and gas behaviour to constrain and sanity-check model output
Modelling
- Build and validate emission event detection, duration estimation, volume quantification, and causation models against real customer data
- Engineer features that encode process and domain knowledge, not just statistical signal
- Design validation strategies where ground truth is incomplete or absent — time-holdouts, physical plausibility checks, engineering corroboration
- Quantify and communicate uncertainty: volumes as ranges with stated confidence, not single numbers
- Treat noisy, drifting, gap-ridden sensor data as a first-class part of the problem, not a preprocessing nuisance
Ownership & Collaboration
- Own model direction for the accounts you carry, from raw data through to the output a customer sees
- Present findings and methodology to customers' technical teams
- Contribute to the shared modelling approach across accounts — what generalizes, what has to be facility-specific
- Support deployment, monitoring, and retraining of what you build
What We're Looking For
Required
- Strong oil and gas facility and process understanding. You know how upstream or midstream sites work — tanks and their venting pathways (thief hatches, relief valves, vapour recovery), flares and combustors, separators, compressors, blowdowns. This is the hard requirement, not a bonus.
- Engineering degree — chemical, mechanical, petroleum, environmental, or process (BSc, MSc, or PhD)
- 3+ years building and shipping models on real industrial, sensor, or process data
- Strong Python and the scientific computing stack (NumPy, Pandas, SciPy, scikit-learn)
- Solid time-series and statistical foundations — anomaly detection, changepoint methods, working with noisy real-world signals
- Experience designing validation where clean labels do not exist
- Works independently. You take an ambiguous problem and a messy dataset and come back with something defensible, without needing the work broken down for you.
- Strong communication — you can explain what the data is saying to a facilities engineer, an executive, and a regulator, and be credible with all three
- Comfortable and excited to work in a fast-moving startup environment
Preferred
- Deep learning experience (PyTorch preferred)
- Probabilistic modelling, uncertainty quantification, or Bayesian methods
- Physics-informed or hybrid modelling — physical constraints inside an ML pipeline
- Direct SCADA or historian experience
- Familiarity with emissions regulations (OGMP 2.0, Subpart W, Canadian and US methane regulations)
- Familiarity with cloud data infrastructure (AWS, Supabase/PostgreSQL, or similar)
- P.Eng or working toward it
Where the Work Is Going
The problems currently open or on the near horizon — yours to shape:
- Hybrid models that carry physics-based constraints through an ML pipeline
- Probabilistic estimation of emission volumes with defensible confidence bounds
- Advanced time-series architectures for detection across heterogeneous facility types
- Continuous characterization of normal versus abnormal operating states from SCADA
- Applying language models to regulatory and technical documents — P&IDs, process narratives, filings
What You Aren't
- A data scientist who needs the oil and gas domain explained — this role supplies domain understanding, it doesn't consume it
- A process or facilities engineer who analyses the data and hands the modelling to someone else
- Someone who needs clean, labelled datasets to be productive
- A modeller uninterested in what the data physically represents
- Someone who needs the work broken into defined tasks — we're a startup solving problems that don't have textbook solutions yet
What We Offer
Compensation & Benefits
- Competitive salary and comprehensive benefits
- Direct impact on a product used by major North American energy companies
Culture & Ownership
- Small team, genuine ownership — your work ships to production and shows up in front of customers
- Peer-level technical collaboration on physics-informed modelling
- Flexible work arrangements — downtown Calgary office or fully remote
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Arolytics is an equal opportunity employer. All qualified applicants will be considered for employment.
If this sounds like you, we'd love to hear from you. Apply through Indeed, or send a brief introduction about why Arolytics interests you and your resume to referencing ‘Senior Data Scientist Career" to [email protected]
Application question(s):
- Describe to me how you would use Agentic AI/Gen AI in your workflows to increase your effectiveness but keep quality high.
- Describe to me how you would analyze a PID/PFD?
Experience:
- Oil and Gas: 2 years (required)
Location:
- Calgary, AB T2P 0H3 (preferred)
Work Location: Hybrid remote in Calgary, AB T2P 0H3
