Data Scientist III (US) - Financial Crimes Modeling
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can have Schedule section with "40 hours per week". However we need to ensure we only include Schedule if posting actually states them. It does: Hours: 40. So we can include Schedule. - Benefits: The posting mentions "Our Total Rewards Package" describing benefits, health, retirement, PTO, etc. So we can include Benefits section summarizing that. - About the role: Could be a summary paragraph. The job description includes many paragraphs. We can put About the role as a prose
summarizing the role. But we must not repeat job title as heading. The job title is Data Scientist III (implied). We should not repeat it. - Responsibilities: Could be derived from "Customer Accountabilities", "Shareholder Accountabilities", "Employee/Team Accountabilities". Those are essentially responsibilities. We can combine into Responsibilities section with
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- Requirements: Education & Experience, Preferred Skills, etc. Could be Requirements section.
- Qualifications: maybe similar to Requirements. But we should avoid duplication. The instruction: prefer few substantive sections over many thin ones. So we could have About the role, Responsibilities, Requirements, Benefits, Pay, Schedule. Possibly also Skills? But Skills could be part of Requirements.
We must not include boilerplate like EEO, legal, etc. Also remove duplicated lines, navigation, cookie, etc. Also remove "Work Location:" maybe we can include location in About the role or as a separate meta? The instruction says do NOT put Job ID/requisition numbers as headings — fold minor metadata into a short
or drop it. Work location and hours could be folded into a short
maybe under About the role or separate meta paragraph. But we already have Schedule and maybe we can include location in About the role. We need to preserve ALL substantive information; do not invent or summarize away detail. So we need to keep specifics: e.g., "US Financial Crime Risk Modeling & Advanced Analytics team", "Enterprise Anti-Money Laundering / Counter-Terrorism Financing (AML/CTF) models/AI solutions", etc. Must keep those details. We need to avoid fluff like "commensurate with experience". Not present. We need to output only HTML fragment, no /, no markdown fences. Let's craft:
About the role
Located in New York, NY, this Data Scientist III role provides technical leadership across the Analytics function with an enterprise mandate, supporting the US Financial Crime Risk Modeling & Advanced Analytics team. The role focuses on developing, maintaining, and enhancing Enterprise Anti-Money Laundering / Counter-Terrorism Financing (AML/CTF) models and AI solutions to meet regulatory requirements, support global AML/CTF strategies, address emerging risks, and follow industry best practices. Responsibilities span customer rating, sanctions screening, transaction monitoring, emerging risk, model performance monitoring, analytics, and reporting.
But we need to preserve all substantive information; summarizing may lose detail. However we can keep details in Responsibilities and Requirements sections; About the role can be a brief overview but must not omit substantive info? The instruction: "Preserve ALL substantive information; do NOT invent or summarize away detail." That suggests we should not omit details; we need to keep them somewhere. If we put details in Responsibilities and Requirements, that's okay. About the role can be a short summary but must not omit any substantive info that is not elsewhere. However if we put all details elsewhere, it's okay to have a brief summary as long as we don't lose any info that is not captured elsewhere. But we risk losing some nuance if we only put summary. Safer to put the detailed description in About the role as prose, preserving details. Then Responsibilities could be a list of specific accountabilities. But that may duplicate. However we can keep About the role as a narrative that includes all details, and then Responsibilities maybe not needed. But we need to follow instruction to use sections only when posting has real specific content. It does have responsibilities etc. We could include both but ensure not to omit any detail. Better approach: Use About the role for a concise overview, Responsibilities for bullet list of duties (customer accountabilities etc.), Requirements for education/experience and preferred skills, Benefits for total rewards, Pay for salary range, Schedule for hours. We must ensure we don't lose any detail that is only in the narrative and not captured elsewhere. So we need to check that all details from the job description are covered either in About the role, Responsibilities, Requirements, Benefits, Pay, Schedule. Let's extract details: From job description: - Role: Data Scientist III provides technical leadership across overall Analytics function which may have an enterprise mandate. - Provides deep technical knowledge and expertise in client interactions to explain complex data analysis related material. - Department Overview: US Financial Crime Risk Modeling & Advanced Analytics team within US Financial Crime department responsible for developing, maintaining, and enhancing Enterprise AML/CTF models/AI solutions to comply with regulatory requirements/changes and internal policies, support TD's global AML/CTF strategies, address emerging risks, and be in accordance with best industry practice. - Seeking a Data Science specialist to join to innovate, drive, and support initiatives and business as usual operations in multiple functional areas including, but not limited to, customer rating, sanctions screening, transaction monitoring, emerging risk, model performance monitoring, analytics and reporting. - Depth & Scope: accountable for significant business management area with enterprise-wide impact; enterprise or functional expert requiring broad managerial and deep specialized knowledge at enterprise, business, regulatory, industry levels; undertakes and completes variety of complex initiatives requiring seasoned specialist knowledge and/or integration of cross functional processes; position 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: In lieu of 5+ years of relevant experience, will consider 3+ years of relevant experience; Proficient with Python or equivalent programming language and SQL; Experience in Financial Crimes / Compliance Risk Analytics is a plus; Experience in optimization / testing / tuning of AML/CTF solutions, particularly transaction monitoring systems; Experience with machine learning model development, validation, and deployment; Experience with LLM-based solutions is a plus; Familiarity with cloud platforms (Azure) and modern data stack tools is a plus. - Customer Accountabilities: (list) - Shareholder Accountabilities: (list) - Employee/Team Accountabilities: (list) - Physical Requirements: (list of percentages) - Who We Are: description of TD. - Our Total Rewards Package: description. - Additional Information: colleague development, training & onboarding, interview process, accommodation, EEO statement (to remove). We need to preserve all substantive information. That's a lot. We could include many sections but the instruction says prefer a few substantive sections over many thin ones. However we must preserve all detail; we can put lots of detail in a few sections using prose and lists. We can have: - About the role: includes role overview, department overview, depth & scope, maybe physical requirements? Physical requirements could be separate but could be included in About the role as a paragraph. - Responsibilities: could combine Customer, Shareholder, Employee/Team accountabilities into a list. - Requirements: Education & Experience, Preferred Skills. - Benefits: Total Rewards package description. - Pay: salary range. - Schedule: hours. We must ensure we don't lose any detail from the lists of accountabilities, physical requirements, etc. So we need to include them somewhere. We could put Physical Requirements in About the role as a paragraph, but that may be too long but okay. Alternatively we could add a section "Working Conditions" but not in allowed headings list. The instruction says usefor section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It gives examples but not exclusive; we could use other headings? It says "use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." Likely they expect only those headings. So we should stick to those. Thus we need to fit all details into those sections. We can put physical requirements in About the role or Responsibilities? Probably About the role is fine. We need to avoid repeating job title as heading. Not needed. We must not include boilerplate like EEO, legal, etc. Remove duplicated lines. Now craft HTML. We'll need to escape any HTML special characters? Not needed. Let's build:
About the role
Located in New York, NY, this Data Scientist III role provides technical leadership across the Analytics function with an enterprise mandate. The role sits within the US Financial Crime Risk Modeling & Advanced Analytics team, which develops, maintains, and enhances Enterprise Anti‑Money Laundering / Counter‑Terrorism Financing (AML/CTF) models and AI solutions to meet regulatory requirements, support TD’s global AML/CTF strategies, address emerging risks, and follow industry best practices. The specialist will innovate, drive, and support initiatives and business‑as‑usual operations in areas such as customer rating, sanctions screening, transaction monitoring, emerging risk, model performance monitoring, analytics, and reporting. The position is accountable for a significant business‑management area with enterprise‑wide impact, requiring broad managerial and deep specialized knowledge at the enterprise, business, regulatory, and industry levels. It involves undertaking complex initiatives that demand seasoned specialist knowledge and/or integration of cross‑functional processes, dealing with senior/executive management, working independently on analysis, design, and support of technical data‑management solutions across projects of varying size and complexity, focusing on longer‑range planning (12 months or greater), and managing multiple projects concurrently. Physical work expectations are predominantly sedentary with occasional domestic travel; specific physical‑activity percentages are: never (0%) for international travel, lifting over 25 lbs., squatting, kneeling, crawling, climbing, reaching overhead, pushing, pulling, twisting; occasional (1‑33%) for domestic travel, responding to sounds, standing, walking, moving safely in confined spaces, lifting/carrying under 25 lbs., bending, reaching forward; frequent (34‑66%) for none listed; continuous (67‑100%) for performing sedentary work, performing multiple tasks, operating standard office equipment, concentrating for long periods, applying common sense to standardized problems, reading/writing/comprehending instructions, and basic arithmetic.
That's a lot but preserves details. Now Responsibilities section: combine Customer Accountabilities, Shareholder Accountabilities, Employee/Team Accountabilities. We need to list each bullet. Let's extract those lists: 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