• Works directly with technology teams to integrate data science models into production systems.

• Writes and delivers reports on findings for technical and non-technical audiences.

• Provides critical reviews for algorithm designs.

• Provides core reviews for algorithm implementations.

• Conducts research and publications for the technical community.

• Assesses data and algorithm design recommendations from data scientists and recommends changes to larger and more complex systems.

• Defines data and model governance practices to operationalize standards.

• Formulates mathematical and simulation models of problems, relating constants and variables, restrictions, alternatives, conflicting objectives, and their numerical parameters.

• Performs validation and testing of models to ensure adequacy and reformulate models.

• Manages the formulation of mathematical modeling and optimizing methods to develop and interpret information that assists management with decision making, policy formulation, or other managerial functions.

• Collects and analyzes data and develops decision support software, service, or products.

• Develops and supplies optimal time, cost, or logistics networks for program evaluation, review, or implementation.

• Elicits, captures, and interprets customer problems from multiple perspectives across projects or within a program.

• Establishes and project manages analysis plans for multiple complex work steams.

• Manages to the estimation, planning, analysis, design, and development of projects.

• Holds accountability for integration into larger, multi-disciplined projects, as appropriate.

• Leads and oversees ML strategy and road map planning.

• Works across teams to influence and lead the direction of external teams.

Education and Experience:

Bachelor's degree (or foreign education equivalent) in Network Science, Analytics, Data Science, Advanced Computer Science, Computer Science, Engineering, Information Technology, Information Systems, Mathematics, or a closely related field and five (5) years of experience as a Senior Manager, Data Science (or closely related occupation) researching and building scalable AI solutions using ML and Natural Language Processing (NLP) models and technologies to improve customer experience and drive business results.

Or, alternatively, Master's degree (or foreign education equivalent) in Network Science, Analytics, Data Science, Advanced Computer Science, Computer Science, Engineering, Information Technology, Information Systems, Mathematics, or a closely related field and two (2) years of experience as a Senior Manager, Data Science (or closely related occupation) researching and building scalable AI solutions using ML and Natural Language Processing (NLP) models and technologies to improve customer experience and drive business results.

Or, alternatively, PhD degree (or foreign education equivalent) in Network Science, Analytics, Data Science, Advanced Computer Science, Computer Science, Engineering, Information Technology, Information Systems, Mathematics, or a closely related field and no experience.

Skills and Knowledge:

Candidate must also possess:

• Demonstrated Expertise ("DE") performing advanced statistical analytics to develop and evaluate supervised and unsupervised ML algorithms - Regression, Decision Trees, Neural Networks, Feature Selection, Hyper-Parameter tuning, and ranking models - using Python or ML libraries (scikit-learn, Tensorflow, or PyTorch).

• DE designing and developing NLP solutions to process unstructured and semi-structured text for NLP tasks - Named Entity Extraction (NER), classification, or clustering - using classical NLP or ML methods (Deep Learning (DL) and embeddings); launching ML and DL models in a production environment; and performing A/B testing to assess the efficacy of the ML and AI algorithms.

• DE collecting, processing and analyzing large scale datasets using Python, Pandas, or Spark to preprocess, clean, analyze and visualize both unstructured and structured data.

• DE refactoring production-level code to achieve greater run-time performance and low latency using parallel processing; and prototyping and deploying ML solutions using Cloud-based environments (Google Cloud, Amazon Web Services (AWS) servers or AWS SageMaker).

#PE1M2

Certifications:

Company Overview

Fidelity Investments is a privately held company with a mission to strengthen the financial well-being of our clients. We help people invest and plan for their future. We assist companies and non-profit organizations in delivering benefits to their employees. And we provide institutions and independent advisors with investment and technology solutions to help invest their own clients' money.

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At Fidelity, you'll find endless opportunities to build a meaningful career that positively impacts peoples' lives, including yours. You can take advantage of flexible benefits that support you through every stage of your career, empowering you to thrive at work and at home. Honored with a Glassdoor Employees' Choice Award, we have been recognized by our employees as a top 10 Best Place to Work in 2024. And you don't need a finance background to succeed at Fidelity-we offer a range of opportunities for learning so you can build the career you've always imagined.

Fidelity's hybrid working model blends the best of both onsite and offsite work experiences. Working onsite is important for our business strategy and our culture. We also value the benefits that working offsite offers associates. Most hybrid roles require associates to work onsite all business days of every other week in a Fidelity office.

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