Principal Data Scientist

About the role

Within Fidelity’s Enterprise Technology Artificial Intelligence Center of Excellence (ET AI CoE), you will help shape the next generation of AI at Fidelity, designing and building critical AI capabilities and services that will benefit institutional and retail clients for years to come. You’ll engage directly with business partners to navigate the complexities of artificial intelligence, advanced analytics, and machine learning, delivering scalable, production-ready solutions that drive customer and business value.

Responsibilities

• Engage directly with business partners to navigate the complexities of artificial intelligence, advanced analytics, and machine learning, delivering scalable, production-ready solutions that drive customer and business value.
• Play a vital technical leadership role: mentoring and uplifting other engineers, building collaborative partnerships, and setting the tone for quality and innovation.
• Build collaborative partnerships with business, product, and engineering teams to translate business requirements into real-world next-generation AI-driven solutions.
• Combine expert open-source AI/ML modeling with hands-on technical experience to build algorithms, models, and, occasionally, applications that surface insights and deliver value (revenue and/or cost saving benefits, customer experiences/journeys, and increased efficiencies through automation/optimization).

Requirements

• Advanced degree or equivalent experience (Master’s or PhD) in Computer Science, Machine Learning, Data Science, Engineering, or a related field, or equivalent practical experience.
• Experience designing, building, and deploying AI/ML models in production environments, with deep understanding of MLOps standards (scalable architectures, reproducibility, monitoring, CI/CD, and compliance).
• Extensive hands-on expertise with: Natural Language Processing (NLP) and computational linguistics, including entity recognition, text summarization, and conversational analytics.
• Programming in Python (primary), with experience in modern ML libraries (PyTorch, transformers, etc.).
• Cloud-based workflows (AWS SageMaker, Bedrock, Athena, Snowflake).
• RAG pipelines for retrieval-augmented generation, enabling context-aware responses and integration of external knowledge sources.
• Demonstrated technical leadership: mentoring and upskilling data scientists, conducting peer code/design reviews, driving architectural decisions, and championing engineering excellence.
• Ability to communicate complex technical topics clearly and persuasively to both technical and non-technical partners (including strong technical writing/documentation skills).
• Experience working with audio data pipelines, speech recognition (ASR), audio-to-audio processing, and conversational AI (including dialogue systems, call transcription, or voice assistants).
• Experience with modern real-time and streaming data technologies (such as Apache Kafka, AWS Kinesis, Apache Pulsar, or cloud-native event streaming platforms).
• Hands-on CUDA/GPU optimization experience for accelerating AI/ML workloads.
• Able to decompose research or tactical deliverables into modular, reusable, strategic capabilities/services.
• Excellent communication and presentation skills while being comfortable collaborating in team settings.

Qualifications

• Advanced degree or equivalent experience (Master’s or PhD) in Computer Science, Machine Learning, Data Science, Engineering, or a related field, or equivalent practical experience.
• Experience designing, building, and deploying AI/ML models in production environments, with deep understanding of MLOps standards (scalable architectures, reproducibility, monitoring, CI/CD, and compliance).
• Extensive hands-on expertise with: Natural Language Processing (NLP) and computational linguistics, including entity recognition, text summarization, and conversational analytics.
• Programming in Python (primary), with experience in modern ML libraries (PyTorch, transformers, etc.).
• Cloud-based workflows (AWS SageMaker, Bedrock, Athena, Snowflake).
• RAG pipelines for retrieval-augmented generation, enabling context-aware responses and integration of external knowledge sources.
• Demonstrated technical leadership: mentoring and upskilling data scientists, conducting peer code/design reviews, driving architectural decisions, and championing engineering excellence.
• Ability to communicate complex technical topics clearly and persuasively to both technical and non-technical partners (including strong technical writing/documentation skills).
• Experience working with audio data pipelines, speech recognition (ASR), audio-to-audio processing, and conversational AI (including dialogue systems, call transcription, or voice assistants).
• Experience with modern real-time and streaming data technologies (such as Apache Kafka, AWS Kinesis, Apache Pulsar, or cloud-native event streaming platforms).
• Hands-on CUDA/GPU optimization experience for accelerating AI/ML workloads.
• Able to decompose research or tactical deliverables into modular, reusable, strategic capabilities/services.
• Excellent communication and presentation skills while being comfortable collaborating in team settings.

Skills

• Advanced degree or equivalent experience (Master’s or PhD) in Computer Science, Machine Learning, Data Science, Engineering, or a related field, or equivalent practical experience.
• Experience designing, building, and deploying AI/ML models in production environments, with deep understanding of MLOps standards (scalable architectures, reproducibility, monitoring, CI/CD, and compliance).
• Extensive hands-on expertise with: Natural Language Processing (NLP) and computational linguistics, including entity recognition, text summarization, and conversational analytics.
• Programming in Python (primary), with experience in modern ML libraries (PyTorch, transformers, etc.).
• Cloud-based workflows (AWS SageMaker, Bedrock, Athena, Snowflake).
• RAG pipelines for retrieval-augmented generation, enabling context-aware responses and integration of external knowledge sources.
• Demonstrated technical leadership: mentoring and upskilling data scientists, conducting peer code/design reviews, driving architectural decisions, and championing engineering excellence.
• Ability to communicate complex technical topics clearly and persuasively to both technical and non-technical partners (including strong technical writing/documentation skills).
• Experience working with audio data pipelines, speech recognition (ASR), audio-to-audio processing, and conversational AI (including dialogue systems, call transcription, or voice assistants).
• Experience with modern real-time and streaming data technologies (such as Apache Kafka, AWS Kinesis, Apache Pulsar, or cloud-native event streaming platforms).
• Hands-on CUDA/GPU optimization experience for accelerating AI/ML workloads.
• Able to decompose research or tactical deliverables into modular, reusable, strategic capabilities/services.
• Excellent communication and presentation skills while being comfortable collaborating in team settings.

Benefits

• Comprehensive health care coverage and emotional well-being support.
• Market-leading retirement.
• Generous paid time off and parental leave.
• Charitable giving employee match program.
• Student loan repayment, tuition reimbursement, and learning resources to develop your career.

Pay

The base salary range for this position is $126,000-255,000 USD per year. Placement in the range will vary based on job responsibilities and scope, geographic location, candidate's relevant experience, and other factors. Base salary is only part of the total compensation package. Depending on the position and eligibility requirements, the offer package may also include bonus or other variable compensation.

Schedule

Fidelity’s Onsite Working Model: Fidelity is transitioning to a full-time onsite working model through a phased rollout across regions and roles. Currently, some roles and locations require 100% onsite presence, while others require less. Onsite expectations are likely to evolve as the rollout continues. This transition does not apply to fully remote roles.