Data Scientist

As a Data Scientist , you will collaborate with cross‑functional business units to identify Data Product solutions and monetization opportunities through the adoption of Artificial Intelligence, Data Science, Machine Learning, and Deep Learning, ensuring innovations drive measurable revenue growth and cost optimization. Responsibilities Work closely with Axos Bank business units across Consumer Banking, Commercial Banking, Securities and Clearing, Marketing, Risk management, Fraud Management and Technology COE to develop and enhance models, detection systems, and other related analytics. Explore existing and new data sources as part of the data strategy frame to analytically prove or disprove the value of 3rd party data sources and identify innovative ways of activating revenue. Lead the end‑to‑end model development and production grade deployment lifecycle, including problem definition, data acquisition and preparation, feature engineering, model selection, training, validation, fine tuning, reinforcement learning and performance optimization; oversee deployment to production environments, continuous monitoring, and model retraining; collaborate with business stakeholders to ensure models are aligned to strategic goals and deliver measurable monetization, operational efficiency, and customer value. Design, fine‑tune, and deploy Large Language Models (LLMs) and autonomous AI Agents to perform complex, multi‑step tasks; integrate them with external data sources and APIs; and implement reinforcement learning, prompt engineering, and monetization strategies to drive measurable business impact. Collaborate with data governance and data quality to ensure data products are governed and monitored for quality. Implement data visualization tools and dashboards to facilitate effective communication of complex analytical results. Collaborate with cross‑functional teams to understand business requirements and translate them into data‑driven solutions. Communicate complex analytical findings and insights in a clear and concise manner to non‑technical stakeholders. Requirements Master’s or Ph.D. in computer science or analytics program with 3‑5 years of professional experience in data science OR bachelor’s in computer science or analytics program with 5‑10 years professional experience in data science Statistical Theory and Application Proficiency Proficiency in descriptive and inferential statistics with hypothesis testing design experience. Identify dispersion, and distributions (mean, median, mode, variance, standard deviation, skewness, kurtosis). Apply probability theory and sampling techniques to draw valid conclusions from sample data. Estimate population parameters, calculate confidence intervals, and determine margins of error. Formulate null and alternative hypotheses aligned with business questions. Design and execute statistical tests (t‑tests, ANOVA, chi‑square, non‑parametric tests, etc.) to validate assumptions. Determine sample sizes and statistical power to ensure robust test results. Interpret p‑values, effect sizes, and statistical significance for decision‑making. Experimental Design & A/B Testing, collaborate with product, marketing, and operations teams to design controlled experiments and quasi‑experiments. Implement randomization, control groups, and blocking to reduce bias and confounding effects. Monitor experiments for compliance, data quality, and interim results. Expert knowledge of supervised and unsupervised machine learning techniques, including linear and logistic regression, neural networks, decision trees, random forests, gradient boosted machines, Bayesian methods, and clustering methodologies. Skilled at applying these algorithms to structured and unstructured data for classification, prediction, and segmentation. Experienced in leveraging and fine‑tuning Large Language Models (LLMs) for natural language processing tasks such as text classification, sentiment analysis, entity extraction, summarization, and complex language understanding, enabling the development of AI solutions that combine statistical modeling with advanced NLP. Develop and deploy hybrid AI solutions that integrate traditional machine learning models with LLMs, orchestrating pipelines that utilize both structured data algorithms and state‑of‑the‑art natural language capabilities. Responsible for continuous model evaluation, fine‑tuning, and ensuring alignment with strategic business goals to drive automation, personalization, and enhanced decision‑making. Collaborate cross‑functionally to identify use cases, design model architectures, and implement scalable machine learning workflows that maximize business impact through actionable insights and monetization strategies. Familiarity with Cloud environments (GCP, AWS, Azure, Databricks, Snowflake) Develop and maintain scalable Python‑based data science architectures to support the full machine learning lifecycle, including data ingestion, exploratory analysis, feature engineering, model development, validation, and deployment; utilize libraries such as pandas, NumPy, scikit‑learn, Apache PySpark, TensorFlow, PyTorch, Keras, LLM, Lang Chain, LangGraph to create efficient, reproducible, and production‑ready analytics solutions Seniority level Mid‑Senior level Employment type Full‑time Job Function Information Technology, Engineering, and Science Industries Banking We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI. #J-18808-Ljbffr