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Data Scientist, Fraud Predictive Analytics

Pay
$80,000–175,000/yearAnnual period assumed — pay source
Salary: $80,000.00 - $175,000.00 Pay Type:
Read the full posting
Work setting
Not stated
Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting

What you’ll work on

Full posting
  • Develop data and AI products and models for fraud detection, investigation and prevention

  • Assemble structured and unstructured source data into well-prepared analytical datasets

  • Diagnose and resolve predictive model performance issues

  • Discover and select internal and external data sources for analytical needs

  • Research and recommend practical solutions to financial crime risk exposures

From the employer’s posting
Drives innovation through the development of Data & AI products that can be leveraged across Financial Crimes Unit to build advanced fraud detection, fraud investigation, fraud prevention tools/models.
Applies scripting / programming skills to assemble various types of source data (unstructured, semi-structured, and structured) into well-prepared datasets
Diagnoses and resolves predictive / analytical model performance issues
Works with various data owners to discover and select available data from internal sources and external vendors
Conducts research to identify, assess and recommend viable, practical solutions to specific financial crimes /criminal risk exposures

What you’ll bring

All qualifications

Core experience

  • Experience with programming languages (e.g.
  • SQL, Python, PySpark) and machine learning /deep learning algorithms/packages (e.g.
  • Deep proficiency in statistical analysis, quantitative analytics, forecasting/predictive analytics, multivariate testing, and optimization algorithms.
  • Experience building fraud models (investigation, detection, prevention) for a large Financial Institution
Qualification wording
Experience with programming languages (e.g. SQL, Python, PySpark) and machine learning /deep learning algorithms/packages (e.g. XGBoost, H2O, SparkML).
Deep proficiency in statistical analysis, quantitative analytics, forecasting/predictive analytics, multivariate testing, and optimization algorithms.
Experience building fraud models (investigation, detection, prevention) for a large Financial Institution

About Bank of Montreal

At BMO we are driven by a shared Purpose: Boldly Grow the Good in business and life.

In the employer’s words · Read in context

Apply at Bank of Montreal

Posting record

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Status in our records
Live
First seen by us
Oct 23, 202622 days ago
Recorded sightings
37
Last seen by us
Nov 14, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 22

Similar jobs usually come down between day 8 and day 60.

At Bank of Montreal, jobs usually come down by about day 15.

From 11,862 similar postings that came down, and 147 of Bank of Montreal’s. Coming down is not always a hire.

Bank of Montreal is hiring

39 open jobs

Open jobs by the week we first saw them, last 12 weeks.

  • Hiring is picking up
  • Takes jobs down quickly
  • 10% of open jobs were reposted
Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

  • Python
  • SQL
  • PySpark
  • XGBoost
Where the posting names them
- Advanced degree in Computer Science, Mathematics, Physics, Engineering, Statistics, or other quantitative disciplines and/or equivalent experience - Experience with programming languages (e.g. SQL, Python, PySpark) and machine learning /deep learning algorithms/packages (e.g. XGBoost, H2O, SparkML). - Deep proficiency in statistical analysis, quantitative analytics, forecasting/predictive analytics, multivariate testing, and optimization algorithms.

Your next step

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Demand for Data Scientist roles

New Data Scientist postings at the employers we follow are down 4% over the last 6 weeks compared with the 6 before.

Weekly new Data Scientist postings at the same employers, from the week of August 17 to the week of November 2. The fewest in a week was 1,403, the most 2,949, and the latest week had 2,098.
August 17Last week

Each bar is one week of new postings. The darkest bars are the last 6 weeks.

Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.

About the role

We file this job under Data Scientist, by the work the posting describes.

Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.

Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.

Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.

US government figures for data scientists

Median yearly pay, May 2025
$120,230
Expected change, 2025 to 2035
34.6% more jobsagainst 3.5% for all jobs

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.

Job description

View original posting ↗

Application Deadline:

11/29/2026

Address:

100 King Street West

Job Family Group:

Data Analytics & Reporting

Hybrid role

Applies knowledge of advanced analytic algorithms and technologies (e.g. machine learning, deep learning, artificial intelligence) to deliver better prediction models and/or intelligent automation that enables smarter business decisions, improved customer experience, and drives productivity. Applies strong communication and story-telling skills to summarize statistical/algorithmic findings, draw business conclusions, and present actionable insight in a way that resonates with business/groups. Drives innovation through the development of Data & AI products that can be leveraged across Financial Crimes Unit to build advanced fraud detection, fraud investigation, fraud prevention tools/models.

  •  Conducts research to identify, assess and recommend viable, practical solutions to specific financial crimes /criminal risk exposures
  • Develops and maintains an understanding of the financial crime management processes, frameworks and techniques.
  • Recommends and implements solutions based on analysis of issues and implications for the business.
  • Understands and analyzes complex business problem, then formulates data-driven hypotheses to drive business value.
  • Builds effective relationships with internal/external stakeholders and ensures alignment.
  • Supports data collection, integration, and retention requirements for data.
  • Defines innovative data solutions to loosely defined business problems by leveraging pattern detection over potentially large datasets.
  • Diagnoses and resolves predictive / analytical model performance issues while monitoring system performance and implementation of efficiency improvements.
  • Develops analytical solutions and makes recommendations based on an understanding of the business strategy and stakeholder needs.
  • Works with various data owners to discover and select available data from internal sources and external vendors (e.g. financial crime data) to fulfill analytical needs.
  • Applies scripting / programming skills to assemble various types of source data (unstructured, semi-structured, and structured) into well-prepared datasets with multiple levels of granularities (e.g., demographics, customers, products, transactions).
  • Develops agreed analytical solution by applying suitable statistical & machine learning techniques (e.g., A/B testing, prototype solutions, mathematical models, algorithms, machine learning, deep learning, artificial intelligence) to test, verify, refine hypotheses.
  • Uses the appropriate algorithms to discover patterns.
  • Supports development of tools and delivers training for data analytics and AI.
  • Broader work or accountabilities may be assigned as needed.

Qualifications:

  • Typically between 5 - 7 years of relevant experience in Data Science & Analytics and post-secondary degree in related field of study or an equivalent combination of education and experience.
  • Advanced degree in Computer Science, Mathematics, Physics, Engineering, Statistics, or other quantitative disciplines and/or equivalent experience
  • Experience with programming languages (e.g. SQL, Python, PySpark) and machine learning /deep learning algorithms/packages (e.g. XGBoost, H2O, SparkML).
  • Deep proficiency in statistical analysis, quantitative analytics, forecasting/predictive analytics, multivariate testing, and optimization algorithms.
  • Experience building fraud models (investigation, detection, prevention) for a large Financial Institution

Salary:

$80,000.00 - $175,000.00

Pay Type:

Salaried

The above represents BMO Financial Group’s pay range and type.

Salaries will vary based on factors such as location, skills, experience, education, and qualifications for the role, and may include a commission structure. Salaries for part-time roles will be pro-rated based on number of hours regularly worked. For commission roles, the salary listed above represents BMO Financial Group’s expected target for the first year in this position.

BMO Financial Group’s total compensation package will vary based on the pay type of the position and may include performance-based incentives, discretionary bonuses, as well as other perks and rewards. BMO also offers health insurance, tuition reimbursement, accident and life insurance, and retirement savings plans. To view more details of our benefits, please visit: https://jobs.bmo.com/global/en/Total-Rewards

About Us

At BMO we are driven by a shared Purpose: Boldly Grow the Good in business and life. It calls on us to create lasting, positive change for our customers, our communities and our people. By working together, innovating and pushing boundaries, we transform lives and businesses, and power economic growth around the world.

As a member of the BMO team you are valued, respected and heard, and you have more ways to grow and make an impact. We strive to help you make an impact from day one – for yourself and our customers. We’ll support you with the tools and resources you need to reach new milestones, as you help our customers reach theirs. From in-depth training and coaching, to manager support and network-building opportunities, we’ll help you gain valuable experience, and broaden your skillset.

To find out more visit us at https://jobs.bmo.com/ca/en.

BMO is committed to an inclusive, equitable and accessible workplace. By learning from each other’s differences, we gain strength through our people and our perspectives. Accommodations are available on request for candidates taking part in all aspects of the selection process. To request accommodation, please contact your recruiter.

Note to Recruiters: BMO does not accept unsolicited resumes from any source other than directly from a candidate. Any unsolicited resumes sent to BMO, directly or indirectly, will be considered BMO property. BMO will not pay a fee for any placement resulting from the receipt of an unsolicited resume. A recruiting agency must first have a valid, written and fully executed agency agreement contract for service to submit resumes.

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Pay
Salary: $80,000.00 - $175,000.00 Pay Type:
Location & working pattern

Toronto, ON, CAN

Data Analytics & Reporting Hybrid role Applies knowledge of advanced analytic algorithms and technologies (e.g. machine learning, deep learning, artificial intelligence) to deliver better prediction models and/or intelligent automation that enables smarter business decisions, improved customer experience, and drives productivity. Applies strong communication and story-telling skills to summarize statistical/algorithmic findings, draw business conclusions, and present actionable insight in a way that resonates with business/groups. Drives innovation through the development of Data & AI products that can be leveraged across Financial Crimes Unit to build advanced fraud detection, fraud investigation, fraud prevention tools/models.
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