Full Time Nba Data Science Jobs in Texas (NOW HIRING)

Job Description:

Senior Data Scientist - Applied AI, NLP, and LLM Solutions

Note: Fidelity will not provide immigration sponsorship for this position

Fidelity Workplace Investing is seeking hands-on, builder-oriented Senior Data Scientists with experience in applied AI, natural language processing, large language models, machine learning, and knowledge graph technologies. This position will be based full time in either Westlake, TX or Merrimack, NH.

The Purpose of Your Role

This individual will lead high-profile applied data science and artificial intelligence initiatives across Workplace Investing, working closely with Technology, Product Management, AI/ML Engineering, and others. The role will focus on developing and evaluating AI-based solutions using natural language processing (NLP), large language models (LLM), machine learning (ML), knowledge graphs, agentic AI patterns, and other advanced or emerging techniques. Key assignments may include document processing and information extraction, schema mapping, enterprise assistants, recommender systems, and anomaly detection.

The successful candidate must be comfortable operating in a fast-paced and sometimes ambiguous environment working with current and emerging AI technologies. They will be expected to gather and analyze data from multiple structured and unstructured data sources, develop reliable models and evaluation frameworks, interpret and clearly communicate findings to technical and business audiences. They will support a broad range of applied AI initiatives with the highest degree of quality, partner effectively with engineering teams to move solutions into production, and thrive in a high-performing, collaborative work environment. The ideal candidate combines strong data science fundamentals with product instincts, technical curiosity, and a track record of delivering measurable business impact.

The Skills You Bring

• 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.
• Working knowledge of embedding models, vector representations, semantic similarity clustering, or dimensionality reduction techniques such as t-SNE or UMAP.
• 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.

The Value You Deliver

• Lead the 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
  

How Your Work Impacts the Organization

As a data scientist in Fidelity Workplace Investing, you will contribute to advancing the analytics and data science capability for a variety of employee benefit products and will take the organization to the next level.

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.

Certifications:Category:Data Analytics and Insights

Please be advised that Fidelity's business is governed by the provisions of the Securities Exchange Act of 1934, the Investment Advisers Act of 1940, the Investment Company Act of 1940, ERISA, numerous state laws governing securities, investment and retirement-related financial activities and the rules and regulations of numerous self-regulatory organizations, including FINRA, among others. Those laws and regulations may restrict Fidelity from hiring and/or associating with individuals with certain Criminal Histories.