Applied AI, Data Scientist

Responsibilities

• Support end-to-end delivery of AI solutions: participate in problem framing, exploratory analysis, model development, and measurement of outcomes
• Develop and validate machine learning models under guidance (e.g., classification/regression, time series, anomaly detection, NLP as appropriate)
• Contribute to GenAI solutions by building and improving Retrieval-Augmented Generation (RAG) components (document preparation, chunking, metadata, and retrieval testing) and prompt templates following established patterns
• Perform data analysis and feature engineering: data quality checks, joins, transformations, leakage checks, and creation of reusable features
• Define and track model/LLM quality metrics with support (e.g., accuracy, calibration, stability; groundedness/factuality for LLM outputs), and help maintain evaluation datasets and test cases
• Assist with explainability and documentation: summarize model behavior, limitations, and key drivers; contribute to model cards and audit-ready artifacts
• Work with data engineers and platform teams to operationalize solutions: support pipeline integration, scheduling, monitoring, and troubleshooting
• Apply responsible AI practices: follow privacy/security requirements, handle sensitive data appropriately, and participate in bias/fairness reviews
• Communicate findings clearly: prepare concise readouts, visuals, and recommendations for technical and non-technical audiences

• 3+ years of experience (or equivalent) in data science, analytics, or applied machine learning with demonstrated project delivery
• Strong foundation in statistics and predictive modeling; ability to design validation approaches and interpret results
• Proficiency in Python and SQL; experience with common data science libraries (pandas, Num Py, scikit-learn) and version control (Git)
• Experience working with cloud data platforms and relational databases; familiarity with Snowflake and/or PostgreSQL strongly preferred
• Familiarity with machine learning lifecycle concepts: training/validation splits, feature engineering, reproducibility, and basic monitoring metrics
• Ability to communicate clearly and collaborate in cross-functional teams
• Hands‑on exposure to GenAI/LLM solutions (prompting, embeddings, retrieval/RAG, or evaluation) through work projects or applied learning (preferred)
• Experience with visualization and storytelling tools (e.g., Power BI) to communicate insights (preferred)
• Familiarity with ML/LLM evaluation practices (test sets, error analysis, human review, groundedness checks) (preferred)
• Experience with basic software engineering practices (unit testing, packaging) and/or API integration (FastAPI/Flask) (preferred)
• Financial services experience or experience in regulated environments (model documentation, audit readiness, privacy/security constraints) (preferred)

Below are skills and terms extracted directly from this job posting to improve Applicant Tracking System (ATS) visibility. This unique feature helps candidates tailor their applications more effectively — a feature exclusive to Job Tailor job listings.

• machine learning
• data analysis
• feature engineering
• predictive modeling
• Python
• SQL
• pandas
• Num Py
• scikit-learn
• GenAI

• communication
• collaboration
• problem framing
• exploratory analysis
• documentation
• explainability
• concise readouts
• visual storytelling