Senior Data Scientist

About the role

This position will be based full time in either Westlake, TX or Merrimack, NH. The purpose of your role is to lead high-profile applied data science and artificial intelligence initiatives across Workplace Investing, working closely with Technology, Product Management, AI/ML Engineering, and others.

Responsibilities

• Lead data science and model development components of projects involving large language models, natural language processing, knowledge graphs, and related applied techniques.
• Design, build, and deploy applied AI solutions across NLP, LLMs, document processing, schema mapping, recommendation, and anomaly detection use cases.
• Lead data analysis with diverse scope and complex business and technical challenges.
• Develop best practices for data science, considering the full analytical lifecycle.
• Ensure the delivery of high-quality, trustworthy data science by developing guidelines and rigorous evaluation frameworks for AI/ML solutions.
• Implement new technologies in a production environment with product, IT, and data engineering teams.
• Present reports and findings to senior-level technical and nontechnical audiences.

Requirements

The ideal candidate combines strong data science fundamentals with product instincts, technical curiosity, and a track record of delivering measurable business impact.

Qualifications

• PhD in Computer Science, Information Science, Statistics, or a related STEM discipline with focus on AI, machine learning, natural language processing, deep learning, knowledge graphs, or related methods; OR a Master’s Degree in a related field with 3 or more years relevant professional experience.
• Strong technical foundation in machine learning and statistical modeling, with deeper experience in one or more applied AI areas such as natural language processing, large language models, deep learning, knowledge graphs, or related methods.
• Strong Python and SQL programming skills with demonstrated proficiency in data extraction, data engineering, exploratory analysis, feature engineering, data modeling, pipeline automation, and model evaluation.
• Solid verbal communication, presentation, and technical writing skills with an ability to explain complex data science, statistics, and computer science concepts clearly to nontechnical audiences.
• Experience or working knowledge in one or more applied AI areas such as information retrieval, question answering, chatbot evaluation, retrieval-augmented generation, or agentic AI frameworks.
• Exposure to intelligent document processing use cases, which may include document classification, OCR, key-value extraction, signature or seal detection, annotation strategy and dataset creation, and evaluation of extraction quality.
• Experience in one or more predictive modeling areas such as recommendation systems, ranking models, ensemble methods, anomaly detection, statistical process control, time-series monitoring, threshold strategies, or alert-quality evaluation.
• Experience designing or contributing to AI/ML evaluation and monitoring frameworks, including benchmark datasets, labeled and synthetic test data, model and prompt comparison, precision/recall analysis, error analysis, latency assessment, cost-quality tradeoff analysis, and production monitoring with tools such as Fiddler.

Skills

• PhD in Computer Science, Information Science, Statistics, or a related STEM discipline with focus on AI, machine learning, natural language processing, deep learning, knowledge graphs, or related methods; OR a Master’s Degree in a related field with 3 or more years relevant professional experience.
• Strong technical foundation in machine learning and statistical modeling, with deeper experience in one or more applied AI areas such as natural language processing, large language models, deep learning, knowledge graphs, or related methods.
• Strong Python and SQL programming skills with demonstrated proficiency in data extraction, data engineering, exploratory analysis, feature engineering, data modeling, pipeline automation, and model evaluation.
• Solid verbal communication, presentation, and technical writing skills with an ability to explain complex data science, statistics, and computer science concepts clearly to nontechnical audiences.
• Experience or working knowledge in one or more applied AI areas such as information retrieval, question answering, chatbot evaluation, retrieval-augmented generation, or agentic AI frameworks.
• Exposure to intelligent document processing use cases, which may include document classification, OCR, key-value extraction, signature or seal detection, annotation strategy and dataset creation, and evaluation of extraction quality.
• Experience in one or more predictive modeling areas such as recommendation systems, ranking models, ensemble methods, anomaly detection, statistical process control, time-series monitoring, threshold strategies, or alert-quality evaluation.
• Experience designing or contributing to AI/ML evaluation and monitoring frameworks, including benchmark datasets, labeled and synthetic test data, model and prompt comparison, precision/recall analysis, error analysis, latency assessment, cost-quality tradeoff analysis, and production monitoring with tools such as Fiddler.

Benefits

Fidelity will not provide immigration sponsorship for this position.

Pay

Commensurate with experience.

Schedule

Full-time.