Data Science Bank information
See Toronto, ON salary details
$20.5K - $36.3K
16% of jobs
$47.1K is the 25th percentile. Wages below this are outliers.
$36.3K - $52K
13% of jobs
$67.8K - $83.5K
8% of jobs
The median wage is $95.9K / yr.
$83.5K - $99.3K
8% of jobs
$99.3K - $115K
10% of jobs
$115K - $130.7K
9% of jobs
$140.1K is the 75th percentile. Wages above this are outliers.
$130.7K - $146.5K
9% of jobs
$146.5K - $162.2K
9% of jobs
$162.2K - $178K
8% of jobs
$178K - $193.7K
5% of jobs
How much do data science bank jobs pay per year?
As of Aug 17, 2026, the average yearly pay for data science bank in Toronto, ON is $103,158.00, according to ZipRecruiter salary data. Most workers in this role earn between $48,194.00 and $147,445.00 per year, depending on experience, location, and employer.
To thrive as a Data Scientist in banking, you need strong analytical skills, proficiency in statistics, and a solid foundation in mathematics, typically supported by a degree in a quantitative field. Familiarity with programming languages like Python or R, experience with machine learning libraries, and knowledge of data visualization and big data platforms such as SQL, Hadoop, or Spark are crucial. Exceptional problem-solving abilities, attention to detail, and effective communication skills help you translate complex data insights into actionable strategies for non-technical stakeholders. These skills ensure accurate risk assessment, fraud detection, and data-driven decision-making in the highly regulated financial sector.
A Data Science professional in a bank leverages data analysis, statistical modeling, and machine learning to solve business problems and improve decision-making. Their work often involves analyzing customer behavior, detecting fraud, assessing credit risk, and optimizing marketing strategies. They collaborate with other departments to turn raw data into actionable insights, ensuring the bank remains competitive and compliant with regulations. By building predictive models and dashboards, they help the bank enhance efficiency, profitability, and customer satisfaction.
Data science in banking involves analyzing large volumes of financial data to identify patterns, assess risks, and improve decision-making. Data scientists use tools like Python, R, and machine learning algorithms to develop models that enhance customer experience, detect fraud, and optimize operations within financial institutions.
A data science role in a bank involves applying data analysis, machine learning, and statistical techniques to improve financial services, risk management, and customer insights. Data scientists in banking typically work with tools like Python, R, and SQL, and may require knowledge of banking regulations and financial data. They often collaborate with teams to develop predictive models and automate decision-making processes.
As a Data Scientist in a banking environment, you will frequently collaborate with teams from IT, risk management, marketing, and business strategy to develop data-driven solutions. This might involve translating complex analytical findings into actionable insights for non-technical stakeholders or integrating models into existing business processes. Common challenges include aligning data science objectives with business goals, managing data privacy concerns, and ensuring clear communication across different departments. Building strong relationships and maintaining open communication channels are essential for overcoming these challenges and delivering impactful results.
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