As a Data Scientist, you will work closely with clients and cross functional teams to define project scope, ensure technical feasibility, and drive delivery excellence.
Responsibilities
Mapping the end-to-end data science approach and designing the associated software architecture for projects
Driving the technical scoping and feasibility assessment of new projects
Building strong client relationships by acting as a technical advisor and shaping the direction of current and future engagements
Delivering bespoke algorithms and scalable software solutions that adhere to best practices for high-stakes decision-making
Setting the technical bar for the project team, ensuring the highest standards of code, rigour, and delivery quality (IC leadership)
Design, develop, and deploy machine learning and artificial intelligence solutions that drive business decision-making and operational excellence.
Responsibilities
Develop, validate, deploy, and monitor machine learning models that support forecasting, optimization, customer intelligence, operational decision-making, and business growth.
Design and implement advanced predictive models using supervised and unsupervised learning techniques.
Perform feature engineering, model selection, hyperparameter tuning, and performance evaluation.
Establish model governance processes, including documentation, explainability, fairness assessments, and monitoring.
Translate business challenges into analytical frameworks and data-driven solutions.
Data Science turns Zoox's driving, fleet and business data into decisions and automation. Interns may work on Autonomy Software V&V or Core/Domain Data Science teams.
Compensation
The monthly salary range for this position is $6,500 to $9,500. Compensation will vary based on geographic location and level of education.
Responsibilities
Use coding and analytical skills to derive insights that inform decisions and automation
Test new frameworks and algorithms to improve model performance
Work on a project such as drift monitoring, automated model retraining, predictive modeling, similarity search or statistical testing
Analyze real-world driving data to identify patterns, measure autonomy performance, and inform stack improvements
Contribute to metrics platforms that track miles-per-disengagement, driving behavior clusters, and safety-critical events
Requirements
B.S., M.S., Ph.D., or advanced degree in data science, statistics, computer science, or a related engineering program
Data Scientist focused on Revenue Management & Network Optimization, building end-to-end machine learning solutions for dynamic pricing, demand forecasting, and revenue optimization.
Responsibilities
Turn business challenges into well-defined analytical problems
Build and deploy end-to-end machine learning solutions
Design, train, and maintain models that power revenue optimization
Deliver dynamic pricing and AI-driven revenue optimization at scale
Build and deploy algorithms for demand forecasting, purchase probability, and revenue optimization
A senior, hands-on technical role to drive the next generation of AI-powered fraud detection systems. The role involves technical leadership across predictive modeling, AI agents, LLM-based solutions, real-time decisioning systems, and large-scale data engineering.
Responsibilities
Lead and directly contribute to the design, development, deployment, and optimization of machine learning models for fraud detection, risk scoring, anomaly detection, and related security applications.
Define, build, and guide scalable AI and ML pipeline architecture supporting model training, validation, deployment, monitoring, retraining, and continuous improvement.
Hands-on design and implementation of Generative AI and Agentic AI solutions leveraging Large Language Models (LLMs), retrieval-augmented generation (RAG), reasoning and planning workflows, Chain-of-Thought-style prompting techniques, tool/function calling, LLM-based modular systems, and multi-agent orchestration.
Work with large-scale transactional datasets using distributed computing technologies such as Spark and Databricks.
Implement MLOps best practices, including CI/CD, model governance, observability, experimentation frameworks, and performance monitoring.
Contribute to New York Life's digital transformation by developing and deploying AI and machine learning solutions that enhance efficiency and improve client, agent, and employee experiences.
Execute AI/ML initiatives in partnership with data, technology, product and business teams.
Deliver business-aligned OKRs, ensuring AI initiatives are outcome-driven and tied to enterprise value creation.
Contribute to the end-to-end model lifecycle: data exploration, feature engineering, model training/validation, deployment, monitoring, and iteration.
Design, build, and validate ML models across supervised and unsupervised paradigms (e.g., classification, regression, survival analysis, clustering).
Apply statistical methods and experimentation techniques — including hypothesis testing, causal inference, and A/B testing — to validate model performance.
Develop, implement, and support AI computational and data science solutions—including modelling algorithms, machine learning models, and AI tools—tailored to diverse scientific and business application areas.
Translate complex, unstructured business or scientific problems into appropriate data-driven analytical and computational solutions.
Collaborate with cross-functional stakeholders—including scientists, data scientists, engineers, and business teams—to identify opportunities, understand challenges, and deliver impactful AI-driven recommendations and insights.
Research and develop new predictive models, computational methods, and algorithms, applying techniques such as classification, regression, clustering, natural language processing (NLP), image analysis, graph simulation theory, deep learning, and recommendation systems as needed.
Integrate, process, and analyze data from multiple sources and modalities to ensure comprehensive approaches to problem-solving.
Requirements
Bachelor's
Master's
PhD
Nice to have: Master’s or PhD in a relevant quantitative discipline
Nice to have: publishing predictive modelling results
As an Operations Research/Data Scientist on the Assignments team, you will help build, maintain, and scale the algorithms/models that power our global delivery network.
Responsibilities
Build, maintain, and scale algorithms/models for the global delivery network
Collaborate with senior scientists and software engineers to translate business rules into mathematical formulations
Build predictive models that inform dynamic parameters
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