Senior Data Scientist

How We Work Rigorous. Our models produce defensible, reproducible, physically plausible numbers that go into regulatory filings and board reports. Every calculation has to be auditable. Pragmatic. Real industrial process data has gaps, drift, mislabeling, and inconsistency. We solve for the data operators actually have, not the data we wish they had. Curious. The problems sit across thermodynamics, signal processing, and machine learning. Nobody solves them from one discipline. Role Overview 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. Responsibilities 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 emission pathways at a given 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 Handle noisy, drifting, gap-ridden sensor data as a first-class part of the problem 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 Required Qualifications 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, vndapour 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 (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. Communicates credibly with both engineers and non-technical stakeholders Comfortable in a fast-moving startup Preferred Qualifications 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 Emissions regulation familiarity — OGMP 2.0, Subpart W, Canadian or US methane regulations Cloud data infrastructure (AWS, Supabase/PostgreSQL) P.Eng or working toward it What We're Not Looking For Data scientists who need the oil and gas domain explained to them — this role supplies domain understanding, it doesn't consume it Process or facilities engineers who analyse and hand the modelling to someone else Candidates who need clean, labelled datasets to work Modellers uninterested in what the data physically represents Anyone who needs work broken down into defined tasks before they can start Compensation & Benefits Competitive salary and comprehensive benefits Genuine ownership — a small team where your work ships to production and reaches major energy companies directly Peer-level technical collaboration on physics-informed modelling Flexible arrangements — downtown Calgary office or fully remote Location Calgary-based, with preference for Calgary-area candidates. Remote options available. How to Apply Send a brief introduction explaining your interest in Arolytics, along with your resume, to [email protected], referencing "Senior Data Scientist."

Own end-to-end customer engagements by analyzing raw SCADA data to build defensible emission detection and quantification models. Collaborate with engineers and customers to translate physical process operations into auditable data models.

Requires an engineering degree and 3+ years of experience building models using industrial sensor data, with a mandatory deep understanding of oil and gas facilities. Proficiency in Python and statistical time-series analysis is essential for working with noisy, real-world datasets.

• Comprehensive Benefits • Flexible Arrangements

• PostgreSQLPreferred
• PythonPreferred

Identified from the job description. Confirm important requirements above.

• Python
• NumPy
• Pandas
• SciPy
• Scikit-learn
• Time-series Analysis
• Anomaly Detection
• Oil and Gas Process Understanding
• SCADA
• Mass Balance
• Physics-informed Modelling
• Uncertainty Quantification
• PyTorch
• Probabilistic Modelling
• Bayesian Methods
• PostgreSQL

• Data & Analytics
• Energy
• Engineering
• Environmental & Sustainability
• Science & Research

Minimum education

Bachelor’s degree

Minimum experience

3+ years

Apply by

Mar 3, 2027

Posting language

English

Working hours

40 hours per week

Location requirements

Country, Calgary, Alberta, Canada

Seniority

Mid-Senior level

Application method

Direct apply is available