Top Data Science Jobs in New Orleans, LA

Build and deploy statistical and machine learning models for the Toast platform. Collaborate with product, engineering, and business teams to identify opportunities, develop data-driven solutions, evaluate models, and communicate insights to stakeholders. Work in a fast-paced environment and contribute to production ML systems and experimentation.

Lead design and delivery of scalable ML systems across the full lifecycle for recommendations, demand forecasting, targeting, and personalization. Collaborate with engineers, product, and stakeholders to set architecture, metrics, and production best practices; mentor data scientists; lead experimentation and deploy/monitor models using cloud and MLOps tooling.

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

Lead complex modeling projects combining deep learning, LLMs, and traditional techniques. Prototype, deploy, and monitor production models, mentor data scientists, collaborate with engineering/product, and translate results into business impact for insurance customers.

Lead teams applying advanced data science and machine learning to large-scale claims, clinical, and member data to generate actionable insights that reduce cost of care and improve population health. Manage client relationships, mentor staff, ensure project delivery and quality, develop payer-focused analytics products, present findings to senior stakeholders, and promote continuous improvement and knowledge sharing.

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

Entefy seeks a Senior Data Scientist with a Master's or PhD in a relevant field, and 3+ years of experience. Responsibilities include delivering advanced analytics, leading teams, and extracting insights from datasets.

As a Staff Data Scientist at SentiLink, you will develop fraud detection models, conduct analyses, collaborate across teams, and drive the development of financial risk products.

Owner of analytics for EHR, revenue cycle, and operations: design and run experiments, analyze financial and operational metrics, investigate billing and account issues, and deliver actionable insights to Product, Operations, Finance, and Customer Experience to improve accuracy, reduce friction, and support growth.

This role involves analyzing EHR and insurance claims data to define clinical outcomes, developing AI models, and collaborating with technical experts for manuscript preparation.

Design and deploy scalable data science and generative AI solutions: acquire and ETL data from S3/RedShift/Spark, analyze data quality, build/validate models (ML, NLP, networks), deploy models to production, and mentor teams while collaborating with stakeholders and engineering.

Lead company-level measurement and causal strategy, define MAU×ARPU metrics and semantic mart, set scientific standards for experimentation and analysis, own strategic models (MMM, elasticity, forecasting), partner with engineering to productionize analytics, and raise org-wide scientific maturity through governance, templates, and upskilling.

Lead and deliver end-to-end data science projects: design, build, deploy, and monitor ML/statistical models for credit risk, pricing, collections, and fraud. Partner with cross-functional teams to integrate models into production, set model development standards, mentor engineers, and maintain reliable ML pipelines and monitoring systems.

Own and evolve revenue calibration models tying CX interactions to revenue, design causal inference and experimentation frameworks, build LLM-powered classification/NLP pipelines, productionize models with Analytics Engineers, define segmentation and behavioral signals, and ensure statistical rigor for executive reporting and regulatory defensibility.

Lead and deliver productionized ML solutions across audience, marketing, pharma direct, and employer analytics. Design segmentation, propensity, attribution, and identity models; apply causal inference and experimentation; operationalize models with MLOps; evaluate generative AI/LLM use cases; set technical roadmap and mentor data scientists to drive measurable business impact.

Lead end-to-end development and deployment of predictive and hybrid deep learning/LLM models for insurance use cases. Prototype, validate, and productionize models using modern ML frameworks and MLOps, collaborate with product and engineering, and communicate technical results to stakeholders to drive measurable business impact.

Lead high-impact modeling initiatives using deep learning, LLMs, and traditional methods. Prototype, deploy, and monitor hybrid models on large insurance datasets, drive MLOps standards, and communicate results to nontechnical stakeholders to realize business impact.

Develop, validate, and apply econometric and machine learning models to support utility clients. Clean and analyze large datasets, assess data sufficiency, perform quality control and peer code review, and present findings to nontechnical stakeholders. Balance rigorous modeling with clear written and verbal communication to inform client decisions.

Build, optimize, and deploy machine learning models (especially time series) using Python, PySpark, and cloud tools. Perform feature engineering, evaluate model performance (MAPE, RMSE, R²), and create ETL/data pipelines with integration tools. Work with Palantir Foundry, Google Cloud (BigQuery, Vertex AI), AutoAI, and data warehouses to support large-scale analytics. Collaborate with stakeholders, monitor production models, and continuously improve model accuracy and pipelines.

The role involves leveraging advanced analytics and machine learning to assess and mitigate credit and fraud risks, developing scalable modeling solutions while collaborating with stakeholders.

Support data-driven decision-making by collecting, cleaning, and analyzing structured and unstructured data, performing EDA, assisting in building and validating ML and statistical models, creating visualizations and dashboards, documenting findings, and collaborating with cross-functional teams.

As a Data Scientist/Machine Learning Engineer, you'll finetune models, improve data quality, add new signals, and push solutions to production.

The Data Scientist will build machine learning models to enhance claim audits, optimize performance, and improve healthcare decision-making by analyzing complex data sets.

Build, evaluate, and deploy LLM/ML models and synthetic datasets to improve Community Support. Identify opportunities via data exploration, analyze structured and unstructured data, automate LLM evaluation, personalize experiences, and collaborate cross-functionally to drive product and operational impact.

Design and evaluate causal inference experiments and causal ML models to measure product impact, optimize resource allocation, and inform support operations. Build, deploy, and iterate statistical/ML models, analyze tradeoffs, collaborate cross-functionally, and communicate insights to influence decisions and scale data science capabilities.