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Applied Machine Learning Scientist ; Traditional AI

Job in New York, New York County, New York, 10261, USA
Listing for: TD
Full Time position
Listed on 2026-01-16
Job specializations:
  • IT/Tech
    AI Engineer, Data Scientist
Salary/Wage Range or Industry Benchmark: 76290 - 125260 USD Yearly USD 76290.00 125260.00 YEAR
Job Description & How to Apply Below
Position: Applied Machine Learning Scientist I (Traditional AI)
Location: New York

Applied Machine Learning Scientist I (Traditional AI)

Work Location:
New York, New York, États-Unis d'Amérique

Hours
40

Pay Details
$76,290 - $125,260 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  compensation policies and practices have been designed to allow colleagues to progress through the salary range over time asאַרק the talent in his respective 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 organization needs.
ՔYou 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
Analyses, informations et intelligence artificielle

Job Description

The Applied Machine Learning Scientist I is responsible for providing technical expertise on advance analytics and machine learning across a broad range of analytics functions including data and modelling frameworks, tools, technology, processes and procedures.

This role has a contributing role in the development of AI/ML systems and provides actionable insights to solve business problems.

Department Overview

The Advanced Analytics (AA) team at TD Bank serves as a center of excellence (CoE), providing essential support to US lines of business with their advanced analytics requirements. This dedicated team specializes in creating and implementing sophisticated statistical and Machine Learning and Artificial Intelligence solutions to address a wide range of strategic business challenges across consumer, commercial, and shared services sectors within TD Bank.

The AA team collaborates closely with key stakeholders to identify opportunities for leveraging data Powers insights and predictive models to drive informed decision‑making and optimize business outcomes. By harnessing cutting‑edge analytical tools and techniques, the team helps TD Bank stay ahead of the curve in a rapidly evolving financial landscape. Additionally, they play a vital role in fostering a culture of innovation and continuous improvement by staying abreast of emerging trends and technologies in the field of advanced analytics.

Through their expertise and dedication, the AA team contributes significantly to TD Bank's competitive edge and overall success in the marketplace.

Position Overview

This role is mainly focused on “Traditional AI” (less emphasis on Gen‑AI and LLM), developing and applying a broad set of evolving advanced statistical and AI/ML techniques to solve business problems. It includes gathering, analyzing, and modeling a wide range of data, applying mathematical models/techniques and tools, and utilizing software engineering best practices to develop and implement solutions.

The successful candidate will design and develop leading‑edge techniques and innovative solutions to solve a wide array of business problems and provide actionable insights to solve business problems.

Detailed Accountabilities Include ommonsul>
  • Develop robust and reliable solutions leveraging advanced techniques, such as advanced statistics, machine learning and AI, NLP, geospatial analytics, storytelling & visualization, helping to solve strategic business challenges and enable insightful actions.
  • Leverage a broad stack of technologies and platforms and packages— Python, Azure Data Bricks, PySpark, PyPrin, and more — to reveal the insights hidden within huge volumes of numeric and textual data.
  • Work closely with various stakeholders, to successfully move the project through all phases of the model lifecycle, from ideation and design through data gathering, training, evaluation, control and governance partners review and approval process, implementation, and ongoing monitoring and exceeds, with minimal supervision.
  • Effectively communicate model design and results to senior leaders and business partners, articulate the business problems from a technical/quantitative definition and facilitate key strategic discussions and provide thought…
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