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  • Seen live on Momentum Financial Services Group's site today
  • Senior
Pay
$110k to $125k a year CAD
Location
Toronto, Canada
Open for
12+ days
First seen
Oct 23, 2026

Posting record

No flags

Open 12+ days

The posting's record, oldest first
  1. Momentum Financial Services Group's site dates the posting
  2. Ghostless first sees this listing
  3. Last checked: still up
How we know

It hasn't been reposted, listed twice, or left up unusually long, and the posting doesn't say it's a talent pool.

Ghostless records the day it first sees a posting, so a repost can't reset its age. This one was already up when Ghostless started tracking Momentum Financial Services Group, so its true age is longer.

Last checked Nov 4, 2026.

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What it asks of you

40+ years of experience
“With more than 40 years of experience, we’re the team behind Money Mart—Canada’s largest non-bank branch network—and a leader in financial solutions for underserved communities.” (quoted from the posting)
PhD asked for
“Master’s or PhD in Data Science, Statistics, or a closely related quantitative field.” (quoted from the posting)

What the posting discloses

Pay
$110k to $125k a year CAD
Please note: The salary range for this position is between C$ 110,000 to C$ 125,000. (quoted from the posting)

Not stated: Real vacancy, AI screening.

Why these three

Ontario requires postings from employers with 25 or more staff to state the pay, whether the vacancy exists, and whether AI screens applicants.

Skills mentioned

About the role

Who We Are

At Momentum Financial Services Group, we help people move forward by reimagining how money works for those who need it most. With more than 40 years of experience, we’re the team behind Money Mart—Canada’s largest non-bank branch network—and a leader in financial solutions for underserved communities.

From short-term loans to money transfers and prepaid cards, we power the products, technology, and operations that connect over a million customers a year to the money they need, when they need it.

At MFSG, we come together across teams and departments to create something bigger than ourselves: solutions that remove barriers and give people access to money they might not get anywhere else. Whether you're solving problems, building systems, or shaping strategy, your work fuels real support for real people.

We’ve Got You Covered

Compensation Philosophy: Our strategy is simple—we aim to match the market. We regularly review industry standards to ensure our total rewards package is competitive and fair. This commitment helps us attract and retain talented individuals who share our purpose.

Discretionary Annual Bonus: Enjoy the opportunity for a discretionary bonus based on individual performance and company success.

Comprehensive Benefits: Our benefits include health and dental plans with 100% of the premiums covered. We also offer an Employee Assistance Program to support your mental well-being and provide resources for personal challenges.

Retirement Plans: Plan for your future with our robust retirement savings options, ensuring you’re set for the long haul.

Hybrid Work Environment: Experience the best of both worlds with our hybrid work model, allowing you to balance remote work with in-office. When you're at our corporate head office, enjoy a relaxed and collaborative environment featuring breakout rooms for brainstorming and unwinding, plus a variety of snacks to keep you energized throughout the day.

Perks and Rewards: Enjoy reimbursement for tuition assistance and professional development, discounts through Perkopolis and participate in our rewards and recognition programs to celebrate your contributions.

The Job: Senior Data Scientist

Reporting directly to the Senior Director of Data Science, the Senior Data Scientist will be a strategic partner to the business, combining strong business acumen with expertise in advanced analytics and machine learning to drive measurable results. This role places a strong emphasis on understanding business priorities, identifying opportunities, and translating complex business challenges into practical data science solutions.

Working closely with business, product, marketing, finance, and technology teams, this individual will identify and prioritize use cases based on their potential business impact, develop and implement predictive models, and translate analytical findings into clear, actionable recommendations. Success in this role requires the ability to connect model outcomes to financial and operational performance, communicate effectively with business leaders, and influence decisions that support growth, profitability, and customer value.

What You’ll Do

  • Design, develop, validate, and implement predictive and prescriptive models that address emerging business needs and new initiatives. Continuously improve the overall modeling process, including feature selection and engineering, model development, validation, monitoring, and documentation, to enhance model performance, scalability, and efficiency.
  • Lead the development, implementation, and ongoing enhancement of Customer Lifetime Value (CLV) models to support customer strategy and business decision-making. Partner with Product, Marketing, Finance, and other business teams to translate business needs into modeling solutions, establish performance tracking, monitor model effectiveness, and continuously refine methodologies as customer behavior and business priorities evolve.
  • Partner with business stakeholders across various functions to understand business questions and provide data-driven insights through ad hoc analytics. Analyze complex datasets, identify meaningful patterns and opportunities, and translate analytical findings into actionable recommendations that support business decisions and strategies.
  • Analyze large and complex financial datasets to extract meaningful patterns and features that improve model accuracy and business insights.
  • Ensure that models comply with industry regulations, internal policies, and audit requirements.
  • Monitor model performance over time and implement retraining or improvements as necessary.
  • Lead and support strategic data science initiatives such as portfolio optimization and customer behavior analysis, collaborating with partners across cheque cashing, marketing, cross-country operations, and card services to deliver data-driven solutions that improve risk management, business performance, and customer outcomes.

What You’ll Bring to the Table

Required

  • Master’s degree with 5+ years, or PhD with 2+ years, of hands-on experience in data science, econometrics, statistics, financial economics, or a related quantitative field.
  • Experience developing, validating, implementing, monitoring, and refining predictive models and analytical solutions, preferably within financial services.
  • Strong knowledge of statistical and machine learning techniques, feature engineering, model performance monitoring, and working with large financial or customer datasets.
  • Advanced proficiency in Python and SQL for data analysis, statistical modeling, and machine learning.
  • Strong knowledge of predictive modeling, regression, classification, forecasting, segmentation, and model evaluation techniques.
  • Experience with feature engineering, feature selection, model validation, performance monitoring, and model refinement.
  • Experience working with large-scale customer, transactional, and financial datasets.
  • Strong data visualization and analytical communication skills.

Desired

  • Experience with Customer Lifetime Value (CLV), customer behavior, profitability, forecasting, or similar customer-level modeling.
  • Experience with IFRS 9, credit risk, expected loss, or other financial models involving segmentation, forward-looking forecasts, and ongoing model monitoring is an asset.
  • Experience with CLV methodologies, survival analysis, customer behavior modeling, or profitability/forecasting models.
  • Knowledge of IFRS 9, credit risk modeling, or other financial modeling frameworks.
  • Experience with cloud-based analytics and ML platforms (e.g., SnowFlake, Azure, Databricks).
  • Familiarity with MLOps, model deployment, automated monitoring, and improving end-to-end modeling processes.

Education + Professional Accreditations

Required

  • Bachelor’s degree in a quantitative STEM field such as Computer Science, Data Science, Statistics, Mathematics, Economics, Finance, Engineering, or related discipline.

Desired

  • Master’s or PhD in Data Science, Statistics, or a closely related quantitative field.

Please note: The salary range for this position is between C$ 110,000 to C$ 125,000.

Committed to Equal Opportunity

MFSG is committed to accommodating applicants up to the point of undue hardship during the recruitment, assessment and selection process. If you are selected for an interview, please notify MFSG if you require accommodation in respect of the materials or procedures used at any time during this process. If you require accommodation, MFSG will work with you to determine how to meet your needs.

About MFSG – Our Commitment to Responsible Innovation

At MFSG, we are committed to building innovative solutions grounded in ethical, transparent, and responsible use of data and technology. Aligned with the principles outlined in Canada’s Artificial Intelligence and Data Act (AIDA), we take a proactive approach to ensuring that any AI or data-driven systems we use are safe, fair, and accountable.

This posting is for a current position within our organization, offering the opportunity to contribute to meaningful, responsible innovation that supports our employees, clients, and communities.

We prioritize strong data governance, clear communication around how systems work, and safeguards that reduce risks and protect individuals. Our focus is on developing tools and processes that promote equity, reliability, and trust, supported by ongoing monitoring and continuous improvement.

Joining MFSG means contributing to a future-focused organization that values both innovation and integrity, where your work helps shape solutions that responsibly support our employees, clients, and communities.

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