Data Scientist

Data Scientists make sense of the volumes of complex information now available to the financial services industry. They take data analytics beyond traditional levels typically handled by Data Analysts or Business Analysts. They use mathematical, statistical and programming skills to clean, massage and organize massive datasets to extract meaning and value, allowing business problems to be solved, new data-driven products to be developed, and growth opportunities to be discovered.

ROLE-AT-A-GLANCE
Binoculars
Demand Outlook
*

As organizations increasingly rely on technology to drive their businesses, demand for Data Analysts remains high, both inside and outside the financial services industry.

Diploma
Education & Credentials
*

Graduate degree typically required.

Data chart
Quantitative Skills Required
*
Group of people
Interpersonal Skills & Relationship Management
*
Multiple devices Specialized Technology Skills
*
Desktop area Work Environment

Office

ROLE-AT-A-GLANCE
Binoculars
Demand Outlook
*

As organizations increasingly rely on technology to drive their businesses, demand for Data Analysts remains high, both inside and outside the financial services industry.

Diploma
Education & Credentials
*

Graduate degree typically required.

Data chart
Quantitative Skills Required
*
Group of people
Interpersonal Skills & Relationship Management
*
Multiple devices Specialized Technology Skills *
Desktop area Work Environment

Office


WHAT IT IS

Key Role Dimensions

Making sense of vast quantities of data: Perform data programming and modelling, and build complex, technical algorithms to extract meaning and value from massive sets of data.

Analyzing and effectively communicating results: Core insights derived from analysis are applied to the creation and maintenance of data-driven solutions that contribute to forward-thinking business decisions.

Mathematical and data programming expert: Be an expert or have proficiency in data engineering, computer science, analysis, statistics, mathematics and data programming and modelling.

Focus Areas

A Data Scientist’s skill in analyzing big data has uses throughout much of the financial services sector:

  • Banking: Optimize transaction processing, personalize wealth management advice, improve risk management and strengthen marketing strategies.
  • Insurance: Provide more accurate pricing through real-time analytics.
  • Finance and Asset Management: Strengthen predictive capabilities through analysis of patterns and trends.

WHAT IT TAKES TO SUCCEED

Key Job Accountabilities

Data Scientists generally:

Explore and analyze big data:

  • Architect computing environments and specialized databases.
  • Build algorithms and perform data programming and modelling.
  • Integrate and organize large quantities of structured and unstructured datasets.
  • Apply data mining techniques using sophisticated tools to analyze an organization’s data.
  • Verify the integrity of collected data and analytical findings.

Communicate analytical findings:

  • Prepare written and visual reports and explain results to both technical and non-technical audiences.
  • Propose actionable insights and recommendations that support and drive business decisions.

Identify data-driven opportunities:

  • Improve workplace efficiency via process automation.
  • Apply analytical, technical and math skills to building better prediction systems that will integrate with an organization’s products to create a better customer service relationship.
  • Propose analytics strategies and solutions that can optimizeongoing processes (including working in conjunction with other departments and IT teams in an efficient manner).

Knowledge, Skills & Experience

Must-haves:

  • Expert-knowledge of data mining, warehousing, processing and reporting (e.g. SAS and Teradata)
  • Expert-knowledge of statistical and quantitative theories and their applications
  • Highly skilled in processing and/or reconciliation of information or data
  • Highly detail-oriented
  • Analytical Thinking: Problem-solving and reasoning skills
  • Data Interpretation, Sense-making & Communication Skills: Efficiently communicate complex analytical findings to members of the organization and other key stakeholders
  • Financial analysis skills
  • Statistical analysis and modelling skills

Nice-to-haves:

  • Previous experience as a Business Intelligence Analyst, Statistician, Data Engineer or Data Analyst

Education & Credentials

Intermediate/senior-level positions typically require:

  • Undergraduate university degree
  • Graduate degree

In the fields of:

  • Mathematics
  • Statistics
  • Computer Science
  • Data Science
  • Engineering
  • Business

WHERE IT CAN TAKE YOU

Career Pathways

The Data Scientist role is still evolving, and while the career path is in the process of being defined, a number of potential pathways might be found through:

  • Increased Seniority: You could move into a leadership role, such as Project Manager, Chief Data Scientist, Senior Data Scientist or Lead Data Scientist.
  • Move to a Related Field: You may choose to move into a related area of analytics.

Future Trends & Impacts

There are several external factors and environmental trends that can influence the demand and qualifications for this role:

  • Advances in Technology: Continuous learning and skill upgrades will be needed to keep up-to-date with new information database systems and analytic tools.
  • Lack of Skilled Workers: The shortage of qualified talent for Data Scientist roles may impact workloads at different organizations.
  • Dynamic Financial Markets: Given the pace at which the business landscape can change, Data Scientists are constantly challenged to stay current or ahead of the curve with respect to business intelligence.
  • Business Complexity: Continued dependence on technology innovation will create ongoing demand for all technology roles in the financial services sector.


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