Senior Data Scientist

Job Description:

Job Title: Senior Data Scientist

The Purpose of this Role

Are you interested in joining a fast paced and cutting-edge organization where you can make an immediate impact on the business? Do you want to be an AI/ML advocate, using data to answer important questions and shape strategy for leadership? You can be a part of the Artificial Intelligence Center of Excellence (AI CoE) within Fidelity Workplace Investing (WI) that constantly pushes the potential of data to drive the business forward! At Fidelity Workplace Investing Artificial Intelligence Center of Excellence (WI AI COE), you won't be just tinkering around the edges, you'll be building critical solutions that will benefit our clients and internal stakeholders for years to come.

The Value you Deliver

The WI AI COE is seeking a Senior Data Scientist with experience in AI/ML, Natural Language Processing (NLP), Search, virtual assistants/chatbots, large language models (LLMs), Agents/ Agentic flows, deep learning related technologies, predictive and prescriptive analytics.

The Expertise and Skills You Bring

• Bachelor's/ Master's Degree in Engineering, Computer Science, Mathematics, Computational Statistics, Operations Research, Machine Learning or related technical fields
• 8+ years of prior relevant work experience as a data scientist
• Hands on experience in most of the following: AI/ML Projects, Developing Supervised and Unsupervised Machine Learning Algorithms, Deep Learning Models & Techniques, NLP, LLMs, RAG, Reinforcement Learning, Knowledge Graphs
• Experience in working with finetuning Large Language Models (LLMs), Prompt Engineering, RAG Applications, Vector Databases, Transformer Architectures is a desirable.
• Experience in Multivariate Statistics, Experimentation Setup (hypothesis testing, measurements) is also an added advantage.
• Proficiency in the entire Predictive Modelling life cycle - From exploratory data analysis, data cleaning, Feature Engineering to Model Building and Validation, Operationalization and Scalability and establishing decision strategies.
• Programming skills in Python, SQL, and hands on experience on using cloud technology platforms like AWS, Azure or GCP.
• Exposure to AI-centric tools like PyTorch, Langchain, Langgraphis required.
• Comfort with Git source control and repo-driven development.
• Ability to partner closely with a diverse set of business stakeholders to identify needs and deliver AI/ML solutions.
• Demonstrated expertise in synthesizing and presenting research and technical findings to diverse audiences, with a strong ability to quickly acquire new knowledge and a passion for continuous learning.
• Implement new technologies in a production environment with product, IT, and data engineering teams.

• Bachelor's/ Master's Degree in Engineering, Computer Science, Mathematics, Computational Statistics, Operations Research, Machine Learning or related technical fields
• 8+ years of prior relevant work experience as a data scientist
• Hands on experience in most of the following: AI/ML Projects, Developing Supervised and Unsupervised Machine Learning Algorithms, Deep Learning Models & Techniques, NLP, LLMs, RAG, Reinforcement Learning, Knowledge Graphs
• Experience in working with finetuning Large Language Models (LLMs), Prompt Engineering, RAG Applications, Vector Databases, Transformer Architectures is a desirable.
• Experience in Multivariate Statistics, Experimentation Setup (hypothesis testing, measurements) is also an added advantage.
• Proficiency in the entire Predictive Modelling life cycle - From exploratory data analysis, data cleaning, Feature Engineering to Model Building and Validation, Operationalization and Scalability and establishing decision strategies.
• Programming skills in Python, SQL, and hands on experience on using cloud technology platforms like AWS, Azure or GCP.
• Exposure to AI-centric tools like PyTorch, Langchain, Langgraphis required.
• Comfort with Git source control and repo-driven development.
• Ability to partner closely with a diverse set of business stakeholders to identify needs and deliver AI/ML solutions.
• Demonstrated expertise in synthesizing and presenting research and technical findings to diverse audiences, with a strong ability to quickly acquire new knowledge and a passion for continuous learning.
• Implement new technologies in a production environment with product, IT, and data engineering teams.