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Senior Data Scientist

Arolytics Inc.
Calgary, AB

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Détails du poste

Temps plein

Profil recherché

Scikit-learn
Statistique
Infrastructure infonuagique
Doctorat
PyTorch
Ingénierie
NumPy
Pétrole et gaz
Schémas de tuyauterie et d'instrumentation (P&ID)
Maîtrise
Pandas
Analyse statistique
AWS
Baccalauréat
PostgreSQL
Apprentissage profond
SciPy
Analyse et modélisation quantitatives
Système de contrôle et d'acquisition de données (SCADA)
Science des données
Intelligence artificielle
Analyse statistique des données
Compétences en communication

Description complète du poste

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

---

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

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