Data Scientist

• Own the end-to-end development and deployment of agentic LLM applications that deliver measurable business impact.
• Design, build, evaluate, and ship production-ready AI systems from prototype to production.
• Partner with founders to define product direction and turn business problems into AI solutions.
• Collaborate with Engineering to build scalable AI infrastructure, system architecture, and internal tools.
• Improve system performance, reliability, and user experience through rapid experimentation and iteration.

AI & Data Science – LLMs, Healthcare Insights

• Own retailer onboarding from kickoff through go-live.
• Manage weekly scan data submission health across the full retailer base.
• Serve as the primary point of contact for all retailer and CPG partner communications.
• Publish the weekly health dashboard for program performance.
• Coordinate with Scan Data Engineering to ensure technical setup and test submissions are completed.
• Monitor each submission cycle and communicate directly with retailers on action items.

Data Programs Manager

• Applies data science and machine learning to the analysis of electrical, vibration, and acoustic signals, transforming raw time-series sensor data into actionable diagnostics and predictive insights for rotating industrial equipment.
• Partners with engineering and domain experts to design and deploy production-grade signal processing and ML solutions for predictive maintenance across industrial applications.
• Operates effectively in ambiguous problem spaces where signal quality, environmental noise, and domain constraints require both technical rigor and adaptive thinking.
• Design and develop signal processing pipelines and machine learning models that operate on electrical (current/voltage), vibration, and acoustic time-series sensor data, including symmetrical component analysis, matched filtering, wavelet decomposition, and time-frequency analysis techniques.
• Evaluate algorithm performance using both objective metrics and subjective measures, including integration with speech recognition engines where applicable.
• Perform exploratory data analysis, feature engineering, and signal feature extraction on raw electrical, vibration, and acoustic data to surface fault patterns and anomalies.
• Analyze and interpret signals from electrical asset monitoring systems (motors, generators, pumps) utilizing electrical signature analysis, vibration analysis, and signal processing expertise to support fault isolation and anomaly detection.
• Use cross-sensor asset monitoring data (temperature, speed, load) to characterize and validate signal-derived diagnostics.
• Apply data-driven signal processing methods to characterize and isolate faults at the subsystem, component, and machine level, identifying root causes from spectral, electrical, and vibration sensor data in rotating industrial equipment.
• Contribute to end-to-end ML workflows including data ingestion, model training, inference, and monitoring for drift and degradation in live environments.
• Collaborate with engineering, product, and domain SMEs to translate operational challenges into well-scoped data science solutions.
• Communicate findings, model performance, and business value clearly through visualizations, written documentation, and presentations to technical and non-technical stakeholders.
• Explore and evaluate emerging signal processing and AI techniques, recommending production incorporation where appropriate.

Data Scientist – Predictive Maintenance

• Define and execute TKO’s enterprise-wide data strategy, architecture, governance, and data products
• Own and evolve TKO’s enterprise data warehouse — cloud infrastructure, ingestion pipelines, and integrations
• Ensure platform reliability at TKO’s operating cadence
• Define and operate TKO’s enterprise data governance framework
• Prepare data for monetization and AI consumption
• Build TKO’s internal data product capability
• Attract, develop, and retain world-class data talent; manage the Enterprise Data budget

SVP, Head of Enterprise Data

• Own assigned projects and workstreams from initiation through closeout.
• Develop and maintain project execution plans, schedules, and milestone tracking.
• Drive project progress and accountability across internal teams, vendors, consultants, and external partners.
• Coordinate project activities to ensure alignment with program objectives, timelines, and deliverables.
• Facilitate project meetings, track action items, and follow through to completion.
• Build and maintain project-level schedules aligned to the broader program master schedule.
• Monitor project dependencies and identify impacts to adjacent workstreams.
• Support integration of project schedules into the overall program plan.
• Escalate schedule risks and conflicts proactively while recommending mitigation strategies.
• Coordinate activities across engineering, design, construction, infrastructure, and operational stakeholders.
• Manage communication between project teams, vendors, consultants, and leadership.
• Ensure all parties understand scope, responsibilities, milestones, and expectations.
• Support cross-functional collaboration across the QC Ecosystem and external delivery partners.
• Track project budgets, scope, schedules, risks, issues, and progress.
• Maintain accurate project documentation including meeting notes, decision logs, change requests, and status reports.
• Provide regular project updates and executive-ready reporting to Program Leadership.
• Contribute to phase-gate reviews and readiness assessments.
• Identify project-level risks, constraints, and issues early.
• Drive resolution through collaboration and ownership.

Project Manager – Data Center Program Delivery

• Apply your expertise in quantitative analysis, data mining, and statistical modeling to deliver impactful, objective, and actionable data insights that enable informed business and product decisions
• Drive informed and timely decision-making that improves and optimizes the way our products are created, executed, and adopted
• Collaborate with product managers, engineers, product marketers, and designers
• Utilize AI tools to more efficiently develop scalable data solutions, while maintaining rigorous standards for accuracy and statistical integrity.

Data Scientist, Level 4

• Support the successful delivery of complex, fast-paced data-center and mission-critical facility projects by providing essential project management assistance in coordination, documentation, scheduling, cost tracking, and vendor management.
• Manage and maintain complete project documentation and records.
• Track budgets, process invoices and change orders, and assist with financial forecasting and reporting.
• Support RFPs, coordinate vendors and material deliveries, and align procurement with schedules.
• Develop and update detailed project charters, logistics plans, schedules; monitor milestones, and report impacts.
• Assist with field activities, QA/QC tracking, commissioning, and contractor coordination.
• Organize meetings, prepare agendas and minutes, and ensure clear communication across teams.
• Compile and deliver closeout materials, including as-builts, O&M manuals, and warranties.
• Support client communications, prepare progress reports, and clarify project requirements.
• Uphold safety, quality, and compliance standards throughout all project phases.

Associate Project Manager – Data Centers

• Lead, mentor, and grow a team of three data analysts and engineers, setting the standard for rigor and craft
• Own Stepful's data platform end to end — pipelines, warehouse, transformation layer, and BI tooling — and its reliability and scalability
• Define and maintain the company's core metrics across the student journey (enrollment, engagement, completion, certification, placement) and business performance (CAC, LTV, retention)
• Build dashboards and self-serve analytics that put trustworthy data in every team's hands
• Partner with Growth and Product on experimentation — design, instrumentation, and analysis of A/B tests
• Deliver deep-dive analyses that shape strategic decisions, from program design to budget allocation
• Stay hands-on: write production SQL and Python, review your team's work, and take on the hardest technical problems yourself
• Establish data governance and quality practices that scale with the company

Data Lead

• Research, design, and evaluate detection models that identify automated, fraudulent, and malicious activity across Internet-scale data.
• Dig into massive datasets to uncover the patterns and behaviors that distinguish adversaries from legitimate users.
• Define how detection success is measured, designing metrics and evaluation strategies for problems where ground truth is noisy, delayed, or contested.
• Stay current on emerging AI/ML research and evaluate how new techniques can be applied to our products.
• Partner with ML Engineers, Data Engineers, and Product to take detection approaches from research to production and measure their real-world impact.

Senior Data Scientist, Detection

• Develop and implement NLP, LLM, and generative AI approaches (e.g., RAG, prompt strategies).
• Define agentic workflows and reasoning strategies for multi-step legal tasks.
• Develop retrieval strategies, including hybrid search (semantic + lexical), and evaluation metrics (e.g., relevance, ranking quality).
• Analyze large-scale legal datasets to extract insights and improve model performance.
• Establish best practices for model evaluation, validation, and benchmarking.
• Translate experimental results into clear product recommendations and business impact.
• Collaborate with product, legal experts, and engineers to align solutions with user needs.
• Mentor team members and provide technical leadership in data science and AI.

Senior Data Scientist II

• Own intake and integration of commercial data sources, including specialty pharmacy, REMS, claims, and field/CRM data
• Serve as the primary technical point of contact for data aggregators and external vendors. Hold partners accountable to SLAs and data quality standards; resolve issues when feeds break or quality slips
• Design and maintain the data models, transformations, and consumption layers that downstream analytics, forecasting, and reporting depend on
• Build and operate scalable, credential-safe data pipelines
• Establish and enforce data governance practices: lineage, definitions, access controls, audit trails, and change management
• Partner with Commercial Analytics, Forecasting, Field Operations, Market Access, and IT to ensure data products meet consumer needs and handoffs are clean
• Support compliance with HIPAA, PDMA, aggregate spend, and other regulatory requirements
• Support the build of a data function: documentation, runbooks, and monitoring that outlast any one person on the team

Senior Manager, Data Operations and Governance

• Own the strategic direction of the device data and performance platform
• Define how Palmetto connects with hardware manufacturers through integration partnerships
• Shape data models for performance monitoring and alerting
• Build workflows for field service and asset management teams
• Collaborate across Asset Management, Field Service, Operations, Customer Support, Finance, and Engineering
• Drive alerting logic and performance insights
• Ensure monitoring coverage with new asset types
• Own product strategy, OKRs, roadmap, and quarterly planning
• Encourage AI use for discovery and learning
• Manage stakeholder expectations with clear communication

Senior Product Manager – Device Data, Performance

• Conduct quantitative analysis including hypothesis testing and root-cause analysis on large data sets with more autonomy
• Support the working group by identifying types of information needed for analysis or to inform business questions
• Create data structures/transformations to be leveraged by groups for analysis
• Use statistical analysis and machine learning to develop, maintain, and anticipate considerations in implementation of models that address the right business need
• Use critical thinking to use the right approach for each problem statement
• Anticipate business needs and make continuous improvements to models and processes

Quantitative Analytics Lead Associate

• Build and execute partner plans aligned to Intapp's growth priorities and the partner program — joint value propositions, multi-year collaboration roadmaps, co-innovation agendas, joint go-to-market, and co-selling.
• Translate market and client needs into joint solutions — working with Product, R&D, and Industries to develop data integrations and vertical-AI capabilities that make Intapp solutions, including Celeste, measurably smarter and more firm-specific.
• Drive data and interoperability — enable third-party data integration with Celeste via MCP and agent-to-agent integration, and structure fit-for-purpose commercial mechanics.
• Own and grow executive relationships with assigned partners as their trusted advisor and strategic liaison across Intapp.
• Report on execution and results — partner-sourced, -influenced, and -product revenue, integration certifications, and co-innovation outcomes.
• Engage in client opportunities and sales cycles where partners strengthen our positioning, complete the solution, or open access to key stakeholders.
• Operate the coopetition and governance framework — qualify product partnerships with senior executives across Intapp and facilitate decisions.
• Advance partner mindshare and capability — enablement, integration certifications, and investment in Intapp solutions across partner organizations.
• Anchor the operating rhythm — embed partner plans in the annual and strategic plans of our business units, R&D, and GTM functions, and keep Partner Operations systems current.
• Serve as a thought partner to the wider Alliances & Partners team, contributing to ecosystem strategy, executive partner briefings, and the continuous evolution of Intapp's partner program.

Senior Partner Development Manager, Vertical AI & Data Partners

• Lead a critical portion of IQVIA Digital's measurement portfolio
• Oversee products and services that enable our measurement value proposition
• Maximize the value, adoption, scalability, and commercial success of measurement solutions
• Collaborate with cross-functional teams to define product strategy

Senior Product Manager, Measurement Studies, Platforms & Data Services

• Own the end-to-end program management of strategic initiatives for LiveRamp’s Data Marketplace, from initial scoping and requirements through launch and post-launch optimization, ensuring clear accountability, timelines, and success criteria.
• Coordinate and communicate product and engineering work across multiple teams and stakeholders (Product, Engineering, Sales, Partnerships, Operations, and Support) so everyone is aligned on priorities, milestones, and risks.
• Drive the software development lifecycle for marketplace programs, including ideation, specification, prioritization, design, implementation, testing, launch, and iteration, using data to guide tradeoffs and decisions.
• Partner with Product Management to translate customer, partner, and internal stakeholder needs into actionable roadmaps, well-defined program plans, and clearly documented requirements.
• Build and maintain program plans, dependency maps, and risk registers for complex, cross-functional efforts (such as new marketplace capabilities, large partner integrations, and platform migrations), and keep them current and visible.
• Define, implement, and monitor program health metrics (for example, delivery predictability, cycle time, defect rates, adoption/usage, and operational SLAs) and use these to drive continuous improvement.
• Facilitate effective Agile ceremonies and cross-team rituals (planning, backlog refinement, standups, demos, and retrospectives) to support predictable delivery and high team engagement.
• Run cross-functional incident and issue response for marketplace-impacting problems: organize triage, coordinate short-term mitigation, and ensure follow-up actions and root-cause analysis are completed.
• Identify process gaps and friction points in how marketplace work gets delivered (e.g., handoffs between teams, release processes, partner onboarding workflows) and lead structured efforts to improve or redesign them.
• Create and maintain concise, executive-ready status reporting for your programs, highlighting progress, risks, decisions needed, and impact against goals.
• Champion best practices in program and project management across the Data Marketplace and adjacent teams, helping to standardize tools, templates, and ways of working where it adds value.
• Contribute to a culture of ownership, collaboration, and inclusion by modeling our values and supporting teams through ambiguity, changing priorities, and challenging timelines.

Staff Technical Program Manager, Data Marketplace

• Architect and deliver enterprise AI/ML solutions
• Lead strategic initiatives and mentorship
• Collaborate with product and engineering teams

Senior Data Scientist

• Apply your expertise in quantitative analysis, data mining, and statistical modeling to deliver impactful, objective, and actionable data insights that enable informed business and product decisions
• Drive informed and timely decision-making that improves and optimizes the way our products are created, executed, and adopted
• Collaborate with product managers, engineers, product marketers, and designers
• Think creatively, proactively, and futuristically to identify and size up new opportunities within Snap's long term roadmap for data-scientific contributions
• Conduct machine learning or statistical analyses, and build pragmatic, scalable, and statistically rigorous solutions to deliver actionable insights, accurate predictions, and effective optimizations
• Communicate best practices in quantitative analysis and develop cross-functional partnerships
• Utilize AI tools to more efficiently develop scalable data solutions, while maintaining rigorous standards for accuracy and statistical integrity

Senior Data Scientist, Core

• Partner with Product & Engineering teams to analyze and optimize product engagement and the full customer funnel, including acquisition, conversion, retention, and churn.
• Build and maintain statistical models and analyses related to key product use cases such as A/B tests, cohort analysis, and conversion/retention optimization.
• Design, analyze, and interpret experiments (A/B and multivariate tests) across marketing channels and on-site experiences, ensuring statistical rigor and clear recommendations.
• Translate ambiguous business questions into well-scoped analytical projects with clear success metrics and timelines.
• Collaborate with data engineering and analytics engineering partners to define data requirements and ensure reliable, well-modeled datasets for marketing analytics.
• Clearly communicate findings and recommendations to non-technical stakeholders through presentations, dashboards, and written documentation.
• Contribute to shared analytics and modeling best practices within the data science team, including code quality, validation, and documentation.
• Support junior team members through informal mentorship, code review, and knowledge sharing where appropriate.
• Stay current on industry trends and techniques in marketing analytics, experimentation, and applied data science, and apply them pragmatically to Hone’s context.

Data Scientist jobs in New York

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AI & Data Science – LLMs, Healthcare Insights

Data Programs Manager

Data Scientist – Predictive Maintenance

SVP, Head of Enterprise Data

Project Manager – Data Center Program Delivery

Data Scientist, Level 4

Data Scientist, Level 4

Associate Project Manager – Data Centers

Data Lead

Senior Data Scientist, Detection

Senior Data Scientist II

Senior Manager, Data Operations and Governance

Senior Product Manager – Device Data, Performance

Quantitative Analytics Lead Associate

Senior Partner Development Manager, Vertical AI & Data Partners

Senior Product Manager, Measurement Studies, Platforms & Data Services

Staff Technical Program Manager, Data Marketplace

Senior Data Scientist

Senior Data Scientist, Core

Senior Data Scientist, Product