Associate Data Scientist at Fidelity Bank in Jersey City, NJ

Summary: Fidelity Bank is seeking a curious, driven Associate Data Scientist to strengthen the firm's risk detection and mitigation capabilities. The role involves designing, developing, and deploying advanced analytics and AI/ML solutions to enhance risk visibility and support data-driven decision-making across the enterprise. 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 Required Qualifications: - 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 Preferred Qualifications: - 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 Required Skills: Python, Pandas, Scikit-learn, XGBoost, PyTorch, NLTK, SpaCy, Generative AI, Retrieval-augmented generation (RAG), Graph-RAG, Prompt engineering, AI/ML/DL, Classification, Regression, Natural language processing (NLP), Named entity recognition (NER), Transformers, Embeddings, Sequence models, Time series analysis, Evaluation metrics, Oracle, Snowflake, Data wrangling, Exploratory data analysis, Data visualization, Feature engineering, AWS, SageMaker, RESTful APIs, Data pipelines Benefits: Bonus or other variable compensation, 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