Data Scientist

• Lead the strategy, development roadmap, and lifecycle of data products and platforms
• Define the roadmap for data solutions
• Collaborate with cross-functional teams
• Ensure data products are scalable, reliable, and user-friendly
• Monitor and improve data quality, reliability, and user adoption of data tools
• Define and track success metrics for data products

Staff Data Product Manager

• Own the end-to-end data science roadmap across Consumer, Delivery, and Ads — translating business priorities into a coherent ML strategy with clear milestones and measurable ROI.
• Provide strong technical direction and mentorship to a team of Data Scientists and ML Engineers; establish best practices for model development, evaluation, and deployment.
• Partner with C-suite and senior product leadership to influence product strategy and build organizational confidence in ML-driven decision-making.
• Drive a culture of experimentation: define measurement frameworks, champion A/B testing rigor, and hold the team accountable to business impact.
• Lead the design and continuous improvement of Gopuff's search ranking and query understanding systems, including semantic search and intent modeling.
• Build and scale personalization infrastructure that adapts the customer experience in real time — from homepage carousels to dynamic upsell and cross-sell surfaces.
• Develop next-generation recommendation models (collaborative filtering, two-tower retrieval, contextual bandits) that drive basket size and repeat purchase.
• Partner with Product to define upsell and nudge strategies grounded in behavioral signals and causal inference.
• Work day to day with gopuff engineering teams to bring search and recommendation changes to life
• Own the predictive models powering ETA accuracy, dynamic dispatch, and driver routing that underpin Gopuff's speed promise.
• Apply ML to optimize zone coverage, demand forecasting, and fleet utilization — directly impacting contribution margin.
• Partner with Operations to turn model outputs into actionable tooling for fulfillment center and driver teams.
• Architect and own Gopuff's ad ranking stack — query-ad relevance scoring, multi-objective ranking (revenue × customer experience), and auction mechanics.
• Build CTR/CVR prediction models and closed-loop attribution pipelines for sponsored product, display, and offsite formats.
• Define and improve advertiser-facing ML products: bid optimization, budget pacing, audience targeting, and incrementality measurement.
• Collaborate with the Ads Product and Sales teams to grow advertiser ROI while protecting the organic shopping experience.

Director, Data Science

• Own hands-on execution of a portion of KIPP's annual common assessment development roadmap within Renaissance (Illuminate) DnA, ensuring timely delivery and adherence to strong quality standards.
• Build, update, and duplicate assessments with a high degree of accuracy, publish to regions in alignment with the annual calendar, and troubleshoot system issues in a timely manner.
• Maintain and improve auditing and validation processes to proactively ensure high-quality data in downstream reporting.
• Execute beginning-of-year assessment readiness protocols, including sending communications, tracking training attendance, and monitoring assessment windows.
• Develop and maintain clear documentation, resources, and professional development materials to support consistent regional execution across administration windows.

Manager, Regional Data Operations

• Lead and oversee coding, CDI, charge capture, charge movement, and data quality operations across the organization.
• Develop operational plans, annual goals, and performance metrics focused on coding productivity, accuracy, compliance, and documentation integrity.
• Provide subject matter expertise in ICD-10-CM/PCS, CPT/HCPCS, DRGs, APR-DRGs, APCs, and related coding and billing practices.
• Maintain accountability for coding quality, productivity, accounts receivable performance, and regulatory compliance outcomes.
• Lead, train, and develop coding and CDI managers, lead-level staff, and team members while fostering a high-performing, engaged team culture.
• Direct coding quality, CDI, and data integrity initiatives, including internal and external audit strategy, preparation, review, and response.
• Partner with clinical, finance, revenue cycle, compliance, and operational leaders to improve coding accuracy, documentation integrity, and financial performance.
• Oversee internal and external data reporting, analytics, and regulatory submissions while identifying trends and opportunities for performance improvement.
• Ensure systems supporting coding and data quality functions are optimized, including encoder, abstracting, EHR, and reporting tools.
• Support education for clinical and ancillary teams on documentation requirements, medical necessity, and accurate code assignment practices.

Senior Manager, Coding, CDI & Data Quality

• Lead the development and implementation of a comprehensive biomedical research data governance program across SMPH, including best practices for research data stewardship and lifecycle management.
• Partner with investigators to develop and implement data management and sharing plans for grant applications, ensuring alignment with funding agency requirements and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles.
• Facilitate the discoverability, documentation, and dissemination of research datasets, including support for assigning persistent identifiers (e.g., DOIs), developing metadata standards, and depositing datasets into institutional and national repositories.
• Establish and oversee data quality, data curation, and data stewardship frameworks that promote the accuracy, consistency, completeness, and usability of research data assets.
• Lead the design and implementation of data integration, linkage and secure release methodologies spanning structured, semi-structured, textual, imaging, and other heterogeneous biomedical data sources.
• Develop processes, standards, and tools that improve interoperability and enable the secure integration of research, clinical, public health, and other data domains to support advanced research initiatives while protecting privacy of patients and participants in research studies.
• Guide the development and use of de-identified, limited, and synthetic datasets that support research access while maintaining appropriate safeguards for PHI and compliance with institutional policies.
• Develop and implement methodologies for creating research-ready data products that enable reproducible, secure, scalable, and efficient research workflows.
• Conduct and oversee data quality assessments, validation activities, and data fitness-for-use evaluations to ensure that research datasets meet established governance and scientific standards.
• Serve as a subject matter expert in biomedical data governance, advising investigators, departmental leaders, and research teams on data stewardship, data sharing, metadata standards, and responsible data use.
• Develop and deliver training, educational resources, and consultation services that promote best practices in research data management and governance across SMPH.
• Monitor emerging trends, technologies, and policies related to biomedical data governance, FAIR data practices, research data ecosystems, interoperability standards, and advanced data management methodologies.
• Directly and indirectly supervise staff supporting research data operations, data stewardship programs, data curation activities, and institutional data governance initiatives across SMPH.

Data Governance Program Manager

• Possess knowledge of business concepts, tools, and processes that are needed for making sound decisions in the context of the company's business; ability to apply this knowledge appropriately to various situations.
• Oversee the acquisition and consumption of data from structured and unstructured sources, while ensuring quality and comprehensiveness of data for use in analyzing business activities, detecting patterns, and communicating trends.
• Demonstrate an ability to utilize various methods and techniques, including AI, to frame opportunities and conduct analyses that result in productive decisions.
• Develop predictive data models, quantitative analyses, and implement semantic data capabilities utilizing machine learning techniques.
• Lead data exploration and analytics projects and provide on-going coaching regarding visualizations, data mining, and analytic techniques.
• Demonstrate an ability to tell a story with data and create compelling ways to use data to drive actions.

Workforce Insights Specialist – Data Science

• Own and execute the global sourcing process, systems, and analytics strategy across GE Vernova
• Define and drive the Source‑to‑Pay future‑state operating model, ensuring simplification, standardization, scalability, and strong governance
• Lead enterprise sourcing transformation programs from concept through execution, with measurable business outcomes
• Conduct sourcing maturity assessments across Business Units and lead targeted improvement roadmaps
• Own and evolve the global sourcing systems landscape, including platforms such as SupplierConnect, Sourcegate+, Grid Fusion VMR, Sourcing360, and ERP integrations
• Lead sourcing data ingestion into the enterprise data lake; define common metrics, dashboards, and insights
• Build scalable analytics capabilities to improve decision‑making, savings identification, compliance, and risk visibility
• Define and execute the digital and GenAI roadmap for sourcing, from use‑case identification through deployment and adoption
• Partner with IT, finance, legal, and external vendors to modernize sourcing tools and workflows
• Drive user adoption, change management, and capability building to fully capture technology value
• Lead high‑visibility sourcing initiatives, including: GenAI applications, risk management programs, commodity and category playbooks, should‑costing, and productivity initiatives
• Own CPO‑level governance, including Sourcing Councils, leadership operating rhythms, and enterprise reviews
• Prepare executive‑ready materials for ELT, Board, and senior leadership forums
• Lead, coach, and develop a global team responsible for sourcing transformation, systems, and analytics
• Drive enterprise capability‑building programs (digital, GenAI, sourcing foundations, analytics fluency)
• Embed Lean, Kaizen, and continuous improvement principles across sourcing teams
• Foster a culture of accountability, data‑driven decision making, and innovation

Global Sourcing Systems, Initiatives & Advanced Analytics Leader

• Manages a small team of data scientists and data engineers to research, design, develop, deploy, and maintain machine learning, AI, and agentic AI solutions supporting the Claims organization.
• Communicates project progress, insights, results, and recommendations to business partners and senior leadership.
• Manages project scope, expectations, and timelines.
• Collaborates with Data Engineering, IT, and business partners to operationalize machine learning and AI solutions.
• Follows the model governance process including planning, documentation, validation, deployment, monitoring, and review.
• Oversees model performance and supports ongoing monitoring and continuous improvement of deployed solutions.
• Develops and mentors team members to maintain technical excellence in AI/ML, software engineering, and solution design.

Data Science Manager

• Partner with a cross-functional team of software engineers, distinguished researchers, and solutions architects to drive great decisions through modeling
• Define and drive towards an end state that is based on simplicity and the adoption of digital technologies, cloud hosting, and open source software.
• Distill the details of complex interconnected modeling systems to influence senior business leaders on model strategy, business use, and risks
• Assess, challenge, and at times defend state-of-the-art decision-making systems to internal and regulatory partners
• Shape our practice of building machine learning models, from design through training, evaluation, validation, and implementation
• Oversee development of benchmark and challenger models to stress test critical modeling decisions

Senior Director, Data Science

• Drive key components of the strategy for the Finance Data Platform team
• Manage the lifecycle of how massive data volumes are sourced and published for Finance consumption
• Collaborate with engineering and data analysts

Manager, Product Management – Finance Data

• Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love
• Leverage a broad stack of technologies - Python, Conda, AWS, H2O, Spark, and more - to reveal the insights hidden within huge volumes of numeric and textual data
• Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
• Flex your interpersonal skills to translate the complexity of your work into tangible business goals

Senior Associate, Data Scientist – Bureau Data Strategy

• Project Leadership & Delivery
• Lead data-related projects end-to-end: scoping, planning, execution, and delivery
• Build and own project plans, timelines, budgets, and resource allocation across workstreams
• Establish and refine project operating mechanisms -- status cadences, intake processes, and launch readiness criteria
• Remove blockers and make decisions at the pace the project needs to maintain momentum
• Hold workstreams accountable to delivery timelines while protecting space for necessary discovery
• Drive go/no-go decisions for major project milestones and data platform changes
• Data Strategy & Prioritization
• Help shape the organization's data roadmap, prioritizing initiatives based on business value and impact
• Translate business objectives into data project priorities, working with leadership to align investments with strategic goals
• Conduct landscape and options analysis to inform build-vs-buy and tooling decisions from a business-outcomes perspective
• Own the narrative -- communicate project strategy, progress, and outcomes to leadership through status updates and planning documents
• Stakeholder Management & Organizational Influence
• Serve as the primary point of contact between data teams, compliance, engineering, and business stakeholders
• Build and maintain trusted relationships with cross-functional partners; represent project priorities in stakeholder forums
• Manage up effectively -- proactively surface risks, trade-offs, and resource constraints to leadership with recommended paths forward
• Drive alignment across competing organizational needs through influence, data, and clear communication
• Data Governance & Compliance
• Ensure projects adhere to data governance policies, including data quality, access control, retention, and privacy standards
• Partner with data teams and compliance to align project deliverables with governance frameworks (e.g., GDPR, HIPAA, SOC 2, or internal data policies)
• Guide decisions on data ownership, stewardship, and classification across project workstreams
• Ensure the project maintains compliance with organizational data standards and partners effectively with legal and security teams as needed
• Risk Management & Reporting
• Proactively identify risks and dependencies, and drive mitigation plans before they threaten delivery
• Track and report on project and data governance metrics adoption, data quality issues, policy exceptions as part of regular reporting
• Manage scope and change requests, balancing stakeholder needs against delivery constraints

Project Manager, Data Strategy

• Own and shape the data science roadmap for products within our Customer Development (CD) portfolio, aligned to commercial priorities and business outcomes.
• Lead the design, development, and deployment of diagnostic, predictive, and prescriptive analytics solutions, ensuring robustness, scalability, and interpretability.
• Set modelling and analytical standards across the team, covering classical statistical methods, machine learning, and emerging Generative and Agentic AI capabilities.
• Ensure solutions are industrialised and production-ready, leveraging cloud platforms (Azure, Databricks) and aligned with MLOps best practices.
• Lead, coach, and develop a team of data scientists, supporting both technical growth and career progression.
• Foster a culture of high ownership, continuous improvement, and learning, encouraging experimentation while maintaining delivery discipline.
• Provide technical guidance and review, acting as a mentor and escalation point for complex analytical challenges.
• Partner closely with Product, Engineering, and Business stakeholders to shape problem definitions, prioritise initiatives, and drive adoption of data science solutions.
• Translate complex analytics into clear, actionable insights for senior stakeholders, supporting data-driven decision-making.
• Champion agile ways of working, enabling fast-paced experimentation and iterative delivery.

• Design, build, and scale advanced detection and diagnostic models for solar and storage assets.
• Enable faster detection, fewer truck rolls, and better customer experience.
• Report to the Director of Data Science and partner closely across product management, engineering, and operations to take models from idea to production in Omnidian’s Resolv platform.

Senior Data Scientist

• Define and drive the multi-year product vision for Data Compass Experience and AI.
• Create and maintain a roadmap aligned to Data Compass strategic priorities.
• Own the product strategy and roadmap for Compass AI.
• Lead initiatives including intent-based discovery, multi-agent experiences, chat with metrics, personalized recommendations, ambient AI experiences, and agentic workflows.
• Drive a unified Data Compass experience across catalog, quality, lineage, classification, analytics, and future platform capabilities.
• Enable associates to move seamlessly from question to discovery, trust validation, insight, and action.
• Conduct user research and customer discovery across business and technical audiences.
• Own product requirements, prioritization, roadmap planning, and release execution.

Senior Product Manager, Data Compass Experience – AI

• Conduct data discovery and exploratory data analysis across structured and unstructured data
• Perform data cleansing, normalization, aggregation, and modeling
• Design and build analytic applications in Palantir Foundry, including dashboards and interactive workflows
• Apply advanced analytics and statistical methods such as regression, clustering, hypothesis testing, and A/B testing
• Develop and embed AI/ML capabilities (LLMs, NLP, RAG/GraphRAG) into analytic workflows
• Create clear visualizations and metrics to support executive and operational decision‑making
• Collaborate in an Agile/Scrum environment to rapidly iterate on solutions
• Communicate insights effectively to both technical and non‑technical stakeholders

Data Scientist, Application Developer – Secret Cleared

• Provide leadership and direction for day-to-day execution
• Develop and implement execution plans to maximize business objectives
• Manage Project Managers, General Superintendents, and Superintendents
• Allocate, coordinate, and ensure quality assurance of drafting resources
• Implement productivity improvements and assure project compliance
• Maintain consistency in Project and Field Management processes
• Champion safety and discipline safety violators
• Initiate and hold Project Review meetings
• Approve additional pricing quoted to customers
• Resolve cost-related issues
• Track and report on risk management
• Assure customer satisfaction through project manager/superintendent team

Data Center Operations Manager – Mechanical/Plumbing Construction

• Delegating work and evaluating inventory staff
• Analyzing daily cycle counts and reconciling discrepancies
• Preparing monthly reports and promoting safety
• Conducting daily startup meetings and encouraging team feedback

Data Center Team Lead

• Proactively explore RevenueCat’s data to identify customer problems, opportunities, and product bets.
• Translate ambiguous product and business problems and questions into clear analyses, models, and recommendations.
• Partner with Product Managers to shape roadmaps, not just execute on them.
• Design, build, and ship production-grade predictive and descriptive models that power customer-facing features.
• Define and evaluate statistical approaches for experimentation, benchmarking, and forecasting.
• Communicate insights clearly and persuasively to technical and non-technical audiences, with a focus on customer impact.
• Continuously iterate on shipped models and features based on real-world usage and feedback.

• Learn the payments data landscape — Explore, profile, and understand transaction, customer, and channel data across payment rails (wire, ACH, RTP/instant) to build the foundation for detection models.
• Build fraud-detection ML models — Develop, train, and validate supervised and unsupervised models (classification, anomaly detection, graph/network analysis) that flag fraudulent payments in batch and near-real-time.
• Develop a fraud typology-driven approach — Understand the major categories of payments fraud — account takeover, authorized push payment (APP)/scams, synthetic identity, business email compromise, money mule/laundering patterns — and map each to detection signals and modeling strategies for how to detect and address them.
• Engineer features and data pipelines — Design and maintain reliable feature pipelines (behavioral, velocity, device, network, and aggregate features) that feed models, partnering with data engineering to move from prototype to production.
• Leverage AI to strengthen detection — Identify opportunities to apply modern AI techniques (e.g., deep learning, embeddings, LLMs for unstructured signals, foundation/graph models) to improve fraud coverage and reduce false positives.
• Build explainable AI (XAI) — Apply model-interpretability methods (SHAP, LIME, counterfactuals, reason codes) so fraud analysts, model risk, and regulators can understand why a payment was flagged.
• Measure and communicate impact — Track model performance (precision/recall, false-positive rate, fraud dollars prevented), and clearly present findings and recommendations to both technical and business stakeholders.
• Own projects from inception to delivery — Partner with fraud SMEs, product, and engineering to take detection ideas from hypothesis through deployment and monitoring.
• Stay current — Follow fraud trends, emerging attack patterns, and advances in ML/AI and responsible-AI practices relevant to the banking industry.
• Grow across the data science domains — Build depth in model science, feature science, and insight science, strengthening core skills in programming, math & statistics, distributed computing, and communicating complex results.

Data Scientist jobs in United States

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Staff Data Product Manager

Director, Data Science

Manager, Regional Data Operations

Senior Manager, Coding, CDI & Data Quality

Data Governance Program Manager

Workforce Insights Specialist – Data Science

Global Sourcing Systems, Initiatives & Advanced Analytics Leader

Data Science Manager

Senior Director, Data Science

Manager, Product Management – Finance Data

Senior Associate, Data Scientist – Bureau Data Strategy

Project Manager, Data Strategy

Data Science Manager

Senior Data Scientist

Senior Product Manager, Data Compass Experience – AI

Data Scientist, Application Developer – Secret Cleared

Data Center Operations Manager – Mechanical/Plumbing Construction

Data Center Team Lead

Senior Data Scientist

Senior Data Scientist