Data Scientist II

TD•Toronto, ON

8d•CA$81,600 - CA$115,200•Onsite

About The Position

As a Data Scientist II in Business Banking, you will be responsible for designing, developing, and delivering advanced analytics solutions that support data-driven decision making. You will collaborate closely with business owners, technology partners, and cross-functional teams to understand objectives, identify data needs, and deliver actionable insights that drive strategic improvement. This role combines strong technical expertise, analytical thinking, and business partnership to translate complex business problems into scalable data assets, insights, and automated analytics that deliver measurable outcomes across operations and performance management.

Requirements

Undergraduate or advanced technical degree (e.g., math, statistics, engineering, finance, or computer science); graduate's degree preferred with progressive work experience
3+ years of relevant experience in analytics, data science and/or consulting, ideally in an influencing or advisory capacity.
Demonstrated knowledge and application of analytical languages/tools/platforms such as Python / Pyspark, SQL, Tableau, PowerBI, Databricks, on large datasets with a track record of delivering business impact and results.
Strong problem-solving skills, with the ability to navigate ambiguous issues and deliver well-defined solutions.
Excellent communication skills, with experience presenting insights to executive leaders and business partners.
Able to participate as part of a team with targeted deliverables, working on initiatives designed to improve decisions, processes, systems, and applications which support our businesses.

Nice To Haves

SAS skills is considered an asset.
Experience developing data-driven strategies, analytical frameworks, or decision-support solutions is considered a strong asset.

Responsibilities

Leverage analytics, statistical modeling, machine learning, and data mining techniques to uncover insights, support data-driven decision-making, and optimize business outcomes.
Partner with business stakeholders to understand objectives, gather requirements, and translate business needs into analytical solutions.
Use programming languages (Python, Pyspark, SQL) to extract and prepare data, apply statistics and various advanced analytics along with business acumen to inform recommendations and insights from large datasets.
Visualize insights from data to illustrate stories that clearly convey the meaning of results to decision-makers and stakeholders at every level of technical understanding.
Collaborate with other partners, such as AIML scientists, data and business analysts, and product owners to develop scalable solutions that retain long term benefit to the business.
Clearly communicate the value, principles and math behind the scientific process such as hypotheses testing and statistical validation to drive organizational excellence.
Provide thought leadership and/or industry knowledge for own area of expertise and participate in knowledge transfer within the team and business unit.