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Python Data Analysis Jobs in Montreal, QC (NOW HIRING)

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$22.9K

$103.9K

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How much do python data analysis jobs pay per year?

As of Sep 13, 2026, the average yearly pay for python data analysis in Montreal, QC is $103,933.00, according to ZipRecruiter salary data. Most workers in this role earn between $59,332.00 and $139,107.00 per year, depending on experience, location, and employer.

What is a python data analysis?

A Python Data Analysis job involves using Python programming to collect, clean, analyze, and visualize data for insights and decision-making. Professionals in this role use libraries like Pandas, NumPy, and Matplotlib to manipulate datasets and perform statistical analysis. They may work in various industries, solving business problems, identifying trends, and supporting data-driven strategies. Strong programming skills, data wrangling expertise, and knowledge of analytical techniques are essential for success in this field.

What are the typical responsibilities of someone working in python data analysis?

Professionals in Python Data Analysis are usually responsible for collecting, cleaning, and analyzing datasets to uncover trends and inform business strategies. Their daily tasks often include writing Python scripts, visualizing data, conducting statistical analyses, and preparing reports that summarize their findings for both technical and non-technical audiences. Collaboration with data engineers, business analysts, and project managers is common, ensuring that data solutions align with organizational goals. This role offers opportunities to build expertise in specialized areas, such as machine learning or business intelligence, and can lead to career advancement in data science or analytics leadership positions.

What are the key skills and qualifications needed to thrive in python data analysis, and why are they important?

To thrive in a Python Data Analysis role, you need strong proficiency in Python programming, statistical analysis, and data wrangling, often backed by a degree in computer science, mathematics, or a related field. Familiarity with tools such as Pandas, NumPy, Jupyter Notebook, and visualization libraries like Matplotlib or Seaborn, along with potential certifications in data analysis or Python, is highly valuable. Analytical thinking, attention to detail, and effective communication help translate complex data findings into actionable insights for stakeholders. These capabilities are crucial for transforming raw data into meaningful information that supports business decision-making.

What are the most commonly searched types of Python Data Analysis jobs in Montreal, QC?

The most popular types of Python Data Analysis jobs in Montreal, QC are:

What are popular job titles related to Python Data Analysis jobs in Montreal, QC?

For Python Data Analysis jobs in Montreal, QC, the most frequently searched job titles are:

What job categories do people searching Python Data Analysis jobs in Montreal, QC look for?

The top searched job categories for Python Data Analysis jobs in Montreal, QC are:

Python Data Analysis jobs near you
Infographic showing various Python Data Analysis job openings in Montreal, QC as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 85% Full Time, 11% Part Time, and 2% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $103,933 per year, or $50 per hour.

Assistant Manager, Data Quality & Analytics

Montreal, QC • On-site

IATA
See more jobs at IATAexternal

Full-time

Re-posted 6 days ago

Job description

About the team you are joining

The Assistant Manager - Data Quality & Analytics will support the Turbulence Aware NextGen platform migration by contributing to data quality governance and control, migration readiness, acceptance testing coordination, business analysis, and data science activities.

What your day would be like

Therole will support the Assistant Director, Turbulence Aware NextGen, indesigning and validating the Data Quality Control Module of TA NextGen. Therole will also help ensure that migration activities, comprehensive testing,data quality assurance, and new analytics features are delivered with accuracy,reliability, and full requirements traceability.
Therole will apply analytical and data science techniques, including statisticalprofiling, anomaly detection, data validation, and Python-based analysis, tosupport the monitoring and continuous improvement of data quality across theplatform.


Key Responsibilities

 
1. Data Quality Governance, Assurance & Control
Support the definition and documentation of data quality requirements, including applicable standards, validation rules, business rules, thresholds, and acceptance criteria, to help ensure the reliable and continuous operation of the Turbulence Aware NextGen platform.
Support the Assistant Director in designing and validating the Data Quality Control Module of TA NextGen.
Support the design of the Quality Control Module, including quality KPIs, dashboards, alerts, and data science-based controls for:
- Airline-provided EDR and ACARS data
- Third-party supplemental data
- Output datasets distributed to airline participants
Support issue resolution with airlines, developers, infrastructure teams, vendors, and internal stakeholders.


2. Data Science Support for Data Quality Control
Support the use of data science techniques to improve data quality assurance and control across the TA NextGen platform.
Analyze historical and near-real-time datasets to identify patterns, gaps, inconsistencies, latency issues, and abnormal reporting behavior.
Apply statistical profiling, trend analysis, anomaly detection, and data validation techniques to identify data quality issues and monitor data reliability with automated or semi-automated data quality checks using SQL, Python, or similar tools.
Prepare analysis outputs, notebooks, reports, or dashboards to explain data quality findings to technical and non-technical stakeholders.


3. Data Migration Preparation & Reconciliation
Support pre-migration data profiling and baseline validation activities.
Assist in developing reconciliation frameworks for legacy vs. new platform outputs.
Use SQL, Python, or analytical tools to compare datasets, identify discrepancies, and support reconciliation evidence.
Track and document discrepancies, resolution actions, and sign-off criteria.
Support cutover readiness assessments and post-migration stabilization reviews.


4. Support for Turbulence Aware Platform Migration andNew Feature Delivery
Support the development and validation of new revenue-generating features.
Assist in defining acceptance criteria and validation metrics for nowcast, forecast, and warning products.
Support testing activities by preparing, documenting, and maintaining test scripts, and help track test execution progress.
Help verify that deployed features meet defined quality, latency, and reliability standards.
Assist in preparing documentation and dashboards for customers' onboarding and commercialization.


5. Documentation & Governance
Maintain comprehensive documentation, including:
Data dictionaries
Validation rules
Business rules
Data science assumptions and analytical methods
Test plans and scripts
Reconciliation reports
Analytics feature specifications
Data quality dashboards and KPI definitions

Apply