Associate Data Scientist

Fidelity Investments•Jersey City, NJ

2d•$83,000 - $158,000

About The Position

The Fidelity Risk Group (FRG) organization is seeking a curious, driven Associate Data Scientist to help strengthen the firm’s ability to detect, prevent, and mitigate risk. This role is ideal for early‑career data scientists with solid foundational knowledge and experience in Machine Learning (ML), Artificial Intelligence (AI), and emerging Generative AI techniques. In this role, you will design, develop, and deploy advanced analytics and AI/ML solutions that enhance risk visibility, automate controls, and support data‑driven decision‑making across the enterprise. Your work will directly contribute to managing operational, financial, legal, and compliance risks in a rapidly evolving environment. You will collaborate closely with partners across Risk, Compliance, Legal, and Technology teams, diving into complex challenges and translating them into scalable, production-grade models and tools that materially improve the organization’s risk posture. We’re looking for a thoughtful problem‑solver who is eager to learn, build, experiment, and make an impact.

Requirements

Bachelor’s degree in Data Science, Mathematics, Statistics, Computer Science, Physics, Finance (quantitative), or a related STEM field, plus 5+ years of relevant experience; or Master’s degree in one of the above fields with 1 to 3+ years of relevant experience.
Strong proficiency in Python; experience with libraries/frameworks such as Pandas, Scikit-learn, XGBoost, PyTorch, NLTK, SpaCy, and others.
Hands-on experience building GenAI applications (prompting techniques, RAG & Graph-RAG architectures, evaluation, safety/guardrails).
Solid grounding in AI/ML/DL (e.g., classification, regression, NLP, NER, transformers, embeddings, sequence models, time series, evaluation metrics).
Proven ability to query databases (e.g. Oracle, Snowflake) and wrangle large structured, semi-structured. and unstructured datasets, to support exploratory data analysis, data visualization, data cleaning, feature engineering, and preparation for model ingestion.

Nice To Haves

Experience in NLP: entity recognition, summarization, information retrieval (IR), conversational analytics, Q&A, and OCR.
Practical AWS experience (compute, orchestration, storage, and services).
GPU/CUDA optimization for accelerating training/inference.
Visualization/BI: Tableau, Plotly/Dash

Responsibilities

Participate in the full AI/ML lifecycle: Problem framing, data discovery, feature engineering, modeling, evaluation, deployment, monitoring, retraining, and continuous improvement.
Design and deploy GenAI applications: Build retrieval-augmented generation (RAG) or Graph-RAG pipelines, leverage vector databases (e.g., FAISS, Pinecone), and implement prompt engineering, evaluation frameworks, and safety/guardrails.
Build scalable AI/ML services: Productionize models via RESTful APIs and data pipelines; ensure performance, reliability, and cost efficiency.
Leverage cloud platforms: Develop, deploy, and monitor solutions in AWS (e.g., SageMaker), integrating with data platforms (e.g., Snowflake, Oracle).
Collaborate cross-functionally: Partner with product, engineering, and business teams.
Grow professionally: Join team ideation sessions, contribute to group problem‑solving, learn from peers, participate in design and code reviews, and learn best practices as you help build high‑quality AI/ML products.
Stay current: Research emerging methods (e.g., agents, RLHF, few/one-shot prompting), tools, and frameworks; experiment, build proof of concept, pilot, and operationalize where valuable.