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Contract Python Data Analysis Jobs in Montreal, QC

Desjardins Group

Data Valorization Advisor

Montreal, QC · Hybrid

... analytics * Please note that other combinations of qualifications and relevant experience may be considered * Knowledge of French is required * Proficiency in SQL, SAS or Python and one or more data ...

Jay Analytix

Quantative Developer

Montreal, QC · On-site

Contract About the Role We are seeking a Quantitative Developer with strong expertise in ... Advanced Python, including NumPy, Pandas, and SciPy. * Strong experience with data analysis and ...

Jay Analytix

Quantative Developer

Montreal, QC · Hybrid

Contract About the Role We are seeking a Quantitative Developer with strong expertise in ... Advanced Python, including NumPy, Pandas, and SciPy. * Strong experience with data analysis and ...

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All JobsContract Python Data Analysis JobsContract Python Data Analysis Jobs in Montreal, QC

Contract Python Data Analysis information

What is a contract Python data analysis job?

Contract Python Data Analysis jobs involve working on a temporary or project-based basis to analyze and interpret data using Python programming. Professionals in these roles use Python libraries such as pandas, NumPy, and matplotlib to clean, process, and visualize data for clients or organizations. They may be hired for short-term projects to deliver insights, build reports, or automate data workflows, often collaborating remotely or on-site. Contract positions offer flexibility and the opportunity to work with various industries and datasets.

What are the key skills and qualifications needed to thrive as a contract Python data analyst?

To thrive as a Contract Python Data Analyst, you need strong analytical skills, proficiency in Python programming, and a solid understanding of statistics and data manipulation, often supported by a relevant degree or proven experience. Familiarity with tools such as pandas, NumPy, SQL databases, and data visualization libraries is typically required. Excellent problem-solving, communication, and time management skills help you deliver actionable insights and collaborate effectively with stakeholders. These abilities are essential for extracting, interpreting, and presenting data-driven solutions that support business objectives in a contract-based environment.

What are some common challenges faced by contract Python data analysts, and how can they be addressed?

Contract Python Data Analysts often face challenges such as quickly adapting to new data environments, understanding project-specific requirements, and integrating with existing teams on a temporary basis. To address these, it's important to have strong communication skills, be proactive in clarifying expectations early on, and familiarize yourself with the company's data infrastructure and tools as soon as possible. Additionally, maintaining organized and well-documented code helps ensure smooth handovers and collaboration with other team members.

What is the difference between Contract Python Data Analysis vs Contract Data Scientist?

AspectContract Python Data AnalysisContract Data Scientist
Required SkillsPython, SQL, data visualization, basic statistical analysisPython, R, machine learning, statistical modeling, data visualization
Work EnvironmentProject-based, client sites or remote, short-term contractsConsulting firms, tech companies, research projects, often longer-term
Industry UsageFinance, marketing, healthcare, retailTech, finance, healthcare, academia

Contract Python Data Analysts focus on data cleaning, visualization, and basic analysis using Python, suitable for short-term projects. Contract Data Scientists typically handle advanced modeling, machine learning, and statistical analysis, often requiring broader skill sets. Both roles are in high demand but differ in complexity and scope.

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 Contract Python Data Analysis jobs in Montreal, QC?

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

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

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

Contract Python Data Analysis jobs near you

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

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