Top Data Science Jobs in NYC, NY

Lead strategic analytics across Go-To-Market and Account Management by building scalable analytical frameworks, diagnosing performance issues, developing dashboards and automations, and applying experimentation to identify causal relationships. Translate complex data into business strategy, drive measurable outcomes, and establish analytics best practices, terminology, and documentation across the organization.

Provides technical leadership for document verification machine learning solutions. Oversees the full ML lifecycle, including data strategy, model development, evaluation, deployment, monitoring, and improvement. Mentors data scientists, establishes technical standards, identifies risks, and improves model performance, scalability, and reliability. Partners with engineering, product, and customers to translate needs into modeling strategies and communicates technical decisions to diverse stakeholders.

Lead real-world evidence studies for pharmaceutical clients using complex clinical, molecular, claims, EHR, and registry data. Design observational analyses, derive endpoints, apply survival analysis and causal inference methods, interpret results, and communicate findings to technical and non-technical stakeholders. Develop reusable analytical code and standards while incorporating LLMs and agentic workflows into research processes. Collaborate cross-functionally to improve data quality, products, and RWE methodologies, with a focus on oncology and clinical development.

Lead end-to-end data science and machine learning initiatives, from identifying business use cases and querying data through prototyping, evaluation, deployment, and monitoring. Partner with Product, Engineering, and Machine Learning Engineering teams across areas such as risk, insurance, and payments. Develop predictive models, statistical methods, deep learning, and LLM solutions while contributing reusable data assets, tooling, MLOps strategy, technical reviews, and data-informed business decisions.

Create reproducible data science and machine learning tasks with verified reference solutions, robust tests, and evaluation criteria. Investigate coding-agent failures, identify implementation errors and data leakage, refine task designs, and document requirements and verification steps. The role requires competitive data science experience, mathematical reasoning, programming ability, and practical expertise in implementing and validating solutions.

Develops and maintains enterprise data systems, models, schemas, metadata, governance processes, and data workflows supporting the U.S. Navy. Performs data quality validation, ingestion and transformation, forecasting, scenario analysis, and risk assessment. The role also automates data processing, recommends architecture improvements, collaborates with technical and nontechnical stakeholders, and provides expertise in enterprise data management.

Develop and deploy data science solutions for pharmacy benefits and member experience, including predictive models, generative AI pipelines, and rules-based logic. Extract and analyze large-scale data with Python and SQL, manage model lifecycles, monitor production performance, and communicate insights to clinical and business leaders. Collaborate with data scientists, engineers, and operational stakeholders to solve complex healthcare and PBM problems.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Builds workforce analytics, visualizations, statistical analyses, time-series forecasts, and machine learning models using HR and clinical data. The role predicts staffing needs and employee attrition, analyzes pay and burnout relationships, supports pay equity and recruitment optimization, and translates HR strategies into measurable KPIs. Responsibilities include deploying models on cloud platforms, conducting experiments, protecting employee data, and communicating insights to technical and non-technical stakeholders.

Lead the development of statistical and machine learning models for time-series and geospatial data. Build analytic solutions, datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and reports. Evaluate algorithm performance, optimize classifiers, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders to deliver reliable mission-focused software and data capabilities.

Develops statistical and machine learning models for kinematic behavior detection using live, historical, time-series, and geospatial data. Responsibilities include data integration, ETL, feature engineering, forecasting, anomaly detection, algorithm evaluation, simulation, visualization, reporting, documentation, and stakeholder collaboration. The role also contributes to mission-focused software applications, APIs, data workflows, code reviews, troubleshooting, and agile development in a remote-first environment.

Lead statistical and machine learning model development for time-series and geospatial data supporting the FALCON mission. Responsibilities include feature engineering, forecasting, anomaly detection, ETL, dataset creation, algorithm evaluation, simulation, data visualization, reporting, documentation, and stakeholder collaboration. The role also contributes to software applications, APIs, data pipelines, code reviews, troubleshooting, and iterative agile development.

Lead a small data science team while remaining hands-on in building advertising audience, measurement, ranking, and data-feed models. Set technical direction, apply causal inference and experimental design, build scalable production systems, mentor scientists, and partner with product, engineering, sales, and business development teams. Translate campaign data into trusted measurement products, influence senior leaders and customers, and establish Fetch’s methodology as a market standard.

Develop fraud-detection and identity-risk machine learning models from problem definition through deployment. Investigate fraud patterns, engineer predictive features, evaluate model performance, and monitor drift across batch and real-time decisioning environments. Collaborate with senior data scientists and engineers using Python and PySpark while supporting model documentation, explainability, privacy, validation, and governance.

Develops predictive models, machine learning solutions, data pipelines, statistical analyses, and internal data applications. The role uses Python and SQL Server to analyze structured and unstructured data, applying NLP, classification, regression, clustering, automation, and AI/LLM techniques. Responsibilities include maintaining data science platforms, supporting cross-functional decision-making, and communicating technical findings to non-technical audiences.

Develops and deploys production machine learning models and data products for enterprise use. Responsibilities include data mining, feature engineering, model evaluation, NLP and LLM/RAG solutions, API and pipeline integration, cloud-based deployment, model validation, A/B testing, documentation, and lifecycle management. The role translates business needs into scalable data science solutions and collaborates with product, engineering, and business stakeholders throughout the SDLC.

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.

Collect, integrate, clean, and analyze large datasets supporting Federal Law Enforcement investigations. Develop statistical, graph, anomaly detection, spatial, sentiment analysis, machine learning, AI, and computer vision models. Use Python and SQL to deliver actionable insights through dashboards, visualizations, and briefings. Collaborate directly with customers, contribute to research and development, maintain shared capabilities, and support proof-of-concept and business development initiatives.

Develops statistical, machine learning, deep learning, and NLP solutions for legal and administrative teams. Responsibilities include building regression and classification models, fine-tuning language models, creating RAG pipelines, designing AI workflows, analyzing structured and unstructured data, producing visual reports, and deploying reproducible production systems. The role collaborates with lawyers, business departments, and technical teams to advance the firm’s AI capabilities and deliver actionable insights.

Early-career Data Scientist supporting supply chain transformation through machine learning, forecasting, statistical modeling, data preparation, dashboards, and AI solutions. The role addresses inventory optimization, demand forecasting, production planning, delivery performance, and cost reduction. Responsibilities include developing analytical datasets and workflows with Python and SQL, evaluating models, integrating enterprise data, supporting cloud-based analytics, and communicating insights to business stakeholders in a manufacturing environment.

Design and deliver analyses, reports, and dashboards; translate technical findings for business leaders; perform complex data queries and reconciliation; identify data capabilities and partner with enablement teams; mentor analysts and drive a data-driven culture across functions.

Develops AI and automation solutions for IT asset management, including stakeholder agents, anomaly detection, data quality improvements, predictive asset insights, data modeling, dashboards, and regulatory reporting. Leads resiliency review sessions, recommends actions within risk tolerances, translates business needs into data-rich outputs, and presents findings to diverse audiences. The role also supports infrastructure compliance, process improvement, project planning, and production support.

Conduct research and predictive modeling for Citi Bike and shared mobility programs; build Power BI dashboards, reports, and briefing materials; translate complex data into actionable recommendations; monitor vendor compliance and data quality; document data procedures; maintain internal databases; and collaborate with Operations, Planning, Policy, leadership, and other stakeholders.

Conduct research on how consumer LLMs search, reason, and synthesize information; design experiments and causal inference analyses; support product and engineering decisions with rigorous measurement; and build data pipelines and recurring evaluations that turn evolving research findings into product capabilities.