Director, Data Science

Fidelity Investments • Boston, MA

1d • Hybrid

About The Position

Job Description Director, Data Science Are you interested in joining a fast paced and cutting-edge organization where you can make an immediate impact on the business? The FI AI COE is seeking a Principal Data Scientist who is passionate about solving business problems using Emerging technologies. This role will be part of the Artificial Intelligence Center of Excellence (AI CoE) within Fidelity Institutional (FI) that constantly pushes the potential of data to drive the business forward! As a T-shaped AI professional, you'll bring deep technical expertise while demonstrating strong business acumen and cross-functional collaboration skills. Critically, we need someone who thinks in systems understanding how each change ripples through the entire ecosystem and who approaches every project with a production-ready mindset from day one, not just building shiny demos. The Team The Data Scientists at Fidelity Institutional AI COE develop advanced analytics and artificial intelligence solutions to support a variety of different applications. Our team of high caliber scientists, ML engineers, mathematicians and statisticians use meticulous quantitative approaches to ensure that we are efficiently building algorithms and technology relevant to the business or customer

Requirements

• Minimum Master’s Degree in Engineering, Computer Science, Mathematics, Computational Statistics, Operations Research, Machine Learning or related technical fields
• 8+ years of AI development experience with proven AI/ML project delivery in production environments
• Demonstrated ability to manage multiple concurrent projects in fast-paced environments
• Ability to pick up new knowledge fast and passionate about continuous learning
• T-Shaped Expertise Profile Deep Technical Skills: LLM & Modern AI: Hands-on experience with Large Language Models, prompt engineering, context optimization, and fine-tuning techniques
• AI/ML Engineering: Expert knowledge of statistical models, predictive modeling, time series analysis (regression, classification, clustering, dimension reduction)
• Agentic AI frameworks (multi‑agent orchestration, tool‑use planning, evaluator agents, workflow agents)
• Model alignment, guardrails, safety tuning, hallucination mitigation
• Programming: Advanced proficiency in Python with object-oriented and functional programming paradigms
• Broad Cross-Functional Skills: Business Acumen: Ability to understand financial services domain and translate business needs into technical solutions
• Data Engineering: Production experience with ETL pipeline tools (Airflow, dbt) and big data technologies (Snowflake)
• Deployment & MLOps: Experience deploying and managing applications in cloud environments (AWS preferred)
• Collaboration: Strong communication skills to work effectively with technical and non-technical stakeholders
• Technical Stack Experience: Python Ecosystem: NumPy, Pandas, Scikit-learn, Flask, Pip, Anaconda
• ML/AI Frameworks: Hugging Face, LangChain (or similar)
• Big Data Tools: Spark, Snowflake
• Cloud Platforms: AWS (SageMaker, Lambda, EC2, S3, etc.)
• Data Pipeline Tools: Airflow, dbt, or equivalent orchestration frameworks
• RAG & Vector Databases: Experience with semantic search, embeddings, and vector stores
• Core Competencies Production-First Mindset: Builds production-ready solutions from day on not prototypes that need to be rebuilt
• Considers error handling, edge cases, monitoring, and operational concerns upfront
• Writes clean, maintainable, well-tested code that others can understand and extend
• Prioritizes reliability, performance, and user experience over technical novelty
• Systems Thinking: Understands how changes propagate through complex systems and anticipates second-order effects
• Considers data dependencies, API contracts, backward compatibility, and migration paths
• Evaluates trade-offs holistically balancing technical debt, velocity, and long-term sustainability
• Thinks about failure scenarios, rollback strategies, and graceful degradation

Responsibilities

• AI/ML Innovation & Implementation Design and deploy state-of-the-art AI and ML solutions to accelerate FI business growth with production reliability and scalability as primary considerations
• Develop and optimize Large Language Model (LLM) applications for business use cases that integrate seamlessly into existing systems
• Implement context engineering strategies and prompt optimization techniques
• Build and maintain Retrieval-Augmented Generation (RAG) systems for semantic search and knowledge retrieval
• Research and prototype emerging AI techniques, always evaluating for production viability and system-wide impact
• Systems Thinking & Architecture Analyze and anticipate how AI implementations affect upstream and downstream systems, data flows, and user experiences
• Design solutions considering scalability, maintainability, observability, and failure modes from the outset
• Evaluate technical decisions through the lens of system-wide performance, cost, and operational complexity
• Collaborate with platform, infrastructure, and application teams to ensure seamless integration
• Document system dependencies, data lineage, and architectural decisions for long-term maintainability
• Business Partnership & Strategy Collaborate closely with business stakeholders to deeply understand challenges and translate them into technical solutions
• Define and monitor AI performance metrics aligned with business KPIs
• Balance innovation with pragmatism—prioritizing solutions that deliver measurable business value
• Data Engineering & Architecture Design and implement robust ETL pipelines for both structured and unstructured data
• Build scalable data infrastructure supporting real-time and batch processing needs
• Perform exploratory data analysis to uncover insights and improvement opportunities
• Ensure data quality, governance, and security best practices
• Deployment & Operations Deploy and manage AI/ML models in cloud environments (AWS) with production SLAs in mind
• Establish monitoring systems for model performance, drift detection, and system health
• Optimize model serving infrastructure for latency, throughput, and cost
• Implement MLOps best practices for continuous integration and deployment
• Build with observability, debugging, and incident response capabilities from the start

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
• educational assistance including student loan repayment, tuition reimbursement, and learning resources to develop your career

What This Job Offers

• Job Type: Full-time
• Career Level: Director