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About This Role
Work Location:
New York, New York, United States of America
Hours:
40
Pay Details:
$96,130 \- $155,950 USD
TD is committed to providing fair and equitable compensation opportunities to all colleagues. Growth opportunities and skill development are defining features of the colleague experience at TD. Our compensation policies and practices have been designed to allow colleagues to progress through the salary range over time as they progress in their role. The base pay actually offered may vary based upon the candidate's skills and experience, job\-related knowledge, geographic location, and other specific business and organizational needs.
As a candidate, you are encouraged to ask compensation related questions and have an open dialogue with your recruiter who can provide you more specific details for this role.
Line of Business:
Analytics, Insights, \& Artificial Intelligence
Job Description:
*This role is not eligible for TD work visa support or sponsorship (e.g., H\-1B, F\-1 OPT/STEM OPT, TN or other work visa authorizations). Applicants must have authorization to work in the United States without current or future need for TD sponsorship*
TD's Financial Crime Risk Management (FCRM) organization protects the Bank, its customer and the broader financial system from money laundering, terrorist financing, sanctions evasion, and related criminal activity. Within FCRM, the Know Your Customer (KYC) program ensures the Bank maintains a current, accurate understanding of its customers and the risk they present. The US KYC Customer Risk Rating (CRR) team is the analytical core of that program: we design, enhance, and operate the model and data pipelines that assign each customer a risk rating – the rating that drives due\-diligence intensity, review cadence, and downstream compliance activity across the enterprise. The team combines data science, data engineering, and regulatory acumen, working primarily in Oracle SQL, Python, and Azure/Databricks, and partners closely with model risk, compliance testing, and technology stakeholders.
The Data Scientist III provides technical leadership across the overall Analytics function which may have an enterprise mandate. This role generally provides deep technical knowledge and expertise in client interactions to explain complex data analysis related material.
Depth \& Scope:
- Generally accountable for a significant business management area that typically has enterprise\-wide impact or accountability
- Enterprise or functional expert, requiring broad managerial and deep specialized knowledge at the enterprise, business, regulatory and industry levels
- Undertakes and completes a variety of complex initiatives requiring seasoned specialist knowledge and/or the integration of cross functional processes
- Position typically deals with senior/executive management
- Works independently on activities related to analysis, design and support of technical data management solutions on various projects ranging in complexity and size
- Focuses on longer\-range planning for functional area (e.g. 12 months or greater)
- May manage and prioritize multiple projects at a given time
Education \& Experience:
- Undergraduate degree or advanced technical degree preferred (e.g., math, physics, engineering, finance or computer science) Graduate's degree preferred with either progressive project work experience or
- 5\+ year of relevant experience; higher degree education and research tenure can be counted
Preferred Skills:
- Experience in AML/BSA, KYC, sanctions, or broader financial crime compliance; familiarity with customer risk rating methodologies
- Advanced SQL in Oracle or comparable enterprise RDBMS, including performance tuning of large production queries
- Hands\-on experience with Azure, Databricks, and Spark\-based analytics at scale
- Delivering analytics to non\-technical stakeholders
- Experience operating in a model\-governed environment – model change documentation, impact analysis, and supporting validation
- Experience supporting regulatory exams or internal audit requests
Customer Accountabilities:
- Works closely with business owners to identify opportunities and serves as an ambassador for data science
- Is familiar with the business context and data infrastructure and can translate business problems to viable data science solutions
- Uses a wide range of programing languages (e.g. Python) and techniques for extracting and preparing data, applying statistics and various advanced analytics, along with business acumen to extract insights from the big data
- Visualizes insights from the data to tell and illustrate stories that clearly convey the meaning of results to decision\-makers and stakeholders at every level of technical understanding
- Collaborates with other partners, such as data and business analysts, software engineers, data engineers, and application developers to develop scalable and sustainable data science solutions that retains long term benefit to the business
Shareholder Accountabilities:
- Analytical thought leadership and stays current on developments in data mining and the application of data science
- Solicits and offers ideas for improving business processes through insights with the objective of improving effectiveness and efficiency
- Educates the organization on approaches, such as testing hypotheses and statistical validation of result
- Helps the organization understand the principles and the math behind the scientist process to drive organizational alignment
- Translates up to date information into continuous improvement activities that enhances performance
- Adheres to enterprise frameworks or methodologies that relate to activities for business area
- Ensures respective programs/policies/practices are well managed, meet business needs, comply with internal and external requirements, and align with business priorities
- Participates in cross\-functional/enterprise initiatives as a subject matter expert helping to identify risk/provide guidance for complex situations
- Monitors service, productivity and assesses efficiency levels within own function and implements continuous process/performance improvements where opportunities exist
- Leads/facilitates and/or implements action/remediation plans to address performance/risk/governance issues
- Actively manages relationships within and across various business lines, corporate and/or control functions and ensures alignment with enterprise and/or regulatory requirements
- Keeps abreast of emerging issues, trends, and evolving regulatory requirements and assesses potential impacts
- Maintains a culture of risk management and control, supported by effective processes in alignment with risk appetite
Employee/Team Accountabilities:
- Participates fully as a member of the team, supports a positive work environment that promotes service to the business, quality, innovation and teamwork and ensures timely communication of issues/points of interest
- Provides thought leadership and/or industry knowledge for own area of expertise in own area and participates in knowledge transfer within the team and business unit
- Keeps current on emerging trends/developments and grows knowledge of the business, related tools and techniques
- Participates in personal performance management and development activities, including cross training within own team
- Keeps others informed and up to date about the status/progress of projects and/or all relevant or useful information related to day\-to\-day activities
- Contributes to team development of skills and capabilities through mentorship of others, by sharing knowledge and experiences and leveraging best practices
- Leads, motivates and develops relationships with internal and external business partners/stakeholders to develop productive working relationships
- Contributes to a fair, positive and equitable environment that supports a diverse workforce
- Acts as a brand ambassador for your business area/function and the bank, both internally and/or externally
Physical Requirements:
Never: 0%; Occasional: 1\-33%; Frequent: 34\-66%; Continuous: 67\-100%
- Domestic Travel – Occasional
- International Travel – Never
- Performing sedentary work – Continuous
- Performing multiple tasks – Continuous
- Operating standard office equipment \- Continuous
- Responding quickly to sounds – Occasional
- Sitting – Continuous
- Standing – Occasional
- Walking – Occasional
- Moving safely in confined spaces – Occasional
- Lifting/Carrying (under 25 lbs.) – Occasional
- Lifting/Carrying (over 25 lbs.) – Never
- Squatting – Occasional
- Bending – Occasional
- Kneeling – Never
- Crawling – Never
- Climbing – Never
- Reaching overhead – Never
- Reaching forward – Occasional
- Pushing – Never
- Pulling – Never
- Twisting – Never
- Concentrating for long periods of time – Continuous
- Applying common sense to deal with problems involving standardized situations – Continuous
- Reading, writing and comprehending instructions – Continuous
- Adding, subtracting, multiplying and dividing – Continuous
The above statements are intended to describe the general nature and level of work being performed by people assigned to this job. They are not intended to be an exhaustive list of all responsibilities, duties and skills required. The listed or specified responsibilities \& duties are considered essential functions for ADA purposes.
Who We Are:
TD is one of the world's leading global financial institutions and is the fifth largest bank in North America by branches/stores. Every day, we strive to make every interaction, product, and experience remarkably human and refreshingly simple for over 27 million households and businesses in Canada, the United States and around the world. More than 95,000 TD colleagues bring their skills, talent, and creativity to foster deeper relationships, ensure disciplined execution, and build a simpler, faster banking experience. TD is deeply committed to being a leader in client experience, that is why we believe that all colleagues, no matter where they work, are client facing. Together, we are reimagining what banking can be for our clients, colleagues and communities.
Our Total Rewards Package
Our Total Rewards package reflects the investments we make in our colleagues to help them and their families achieve their financial, physical and mental well\-being goals. Total Rewards at TD includes base salary and variable compensation/incentive awards (e.g., eligibility for cash and/or equity incentive awards, generally through participation in an incentive plan) and several other key plans such as health and well\-being benefits, savings and retirement programs, paid time off (including Vacation PTO, Flex PTO, and Holiday PTO), banking benefits and discounts, career development, and reward and recognition. Learn more
Additional Information:
We’re delighted that you’re considering building a career with TD. Through regular development conversations, training programs, and a competitive benefits plan, we’re committed to providing the support our colleagues need to thrive both at work and at home.
Colleague Development
If you’re interested in a specific career path or are looking to build certain skills, we want to help you succeed. You’ll have regular career, development, and performance conversations with your manager, as well as access to an online learning platform and a variety of mentoring programs to help you unlock future opportunities.
If you’re passionate about helping clients and building deep, lasting relationships, TD offers diverse career paths where you can grow your expertise and make a meaningful impact.
We're committed to your success and foster a respectful workplace where diverse perspectives are valued, everyone has fair opportunities to grow, and you can unlock your full potential to achieve your career goals. Here at TD, we hire and develop the best.
Training \& Onboarding
We will provide training and onboarding sessions to ensure that you’ve got everything you need to succeed in your new role.
Interview Process
We’ll reach out to candidates of interest to schedule an interview. We do our best to communicate outcomes to all applicants by email or phone call.
Accommodation
TD Bank is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, status as a protected veteran or any other characteristic protected under applicable federal, state, or local law.
If you are an applicant with a disability and need accommodations to complete the application process, please email TD Bank US Workplace Accommodations Program at [email protected] . Include your full name, best way to reach you and the accommodation needed to assist you with the applicant process.
Salary Context
This $96K-$155K range is in the lower quartile for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At TD, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($126K) sits 35% below the category median. Disclosed range: $96K to $155K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
TD AI Hiring
TD has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in New York, NY, US. Compensation range: $155K - $200K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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